This is a guest post by Elita Baldridge
Most people aren’t familiar with the challenges of working on a PhD with a disability or chronic illness, and yet there’s a good chance that someone you know is in this situation and isn’t talking about it. This is the first in a series of posts about my experiences completing a PhD with a chronic illness, and about the things that we can do to support our colleagues and students so that they can have the greatest chance at success. Even with the best support in the world, it’s not always possible, but it’s a lot less likely without support.
Introduction to the social model of disability
To give you a little background, I developed a chronic illness during graduate school, eventually being diagnosed with fibromyalgia. Developing a chronic illness gave me a crash course in the social model of disability; here’s the general drift.
A lack of support and accommodations is a major factor in people being unable to function effectively.
The biggest thing that keeps me from functioning effectively at this point (with accommodations) is my chronic illness itself. However, that’s the most disabling factor for me because I had the accommodations available to reduce the impact of my condition as much as possible.
This is the sanitized, short version of some of my symptoms. I don’t particularly like to share these sorts of things because I want to be seen as a good ecologist, not as an inspiring story of an ecologist that has triumphed over terrible odds. An ecologist is a person, a story is a thing. However, I think that it’s important to understand that while your colleague may be cheerful and smiling and upbeat, they may also be hiding a lot, and being kind and providing accommodations is a small thing that can mean the world under the circumstances.
Cognitive dysfunction: This manifests itself in many ways, but when this is severe, I can’t actually think well enough to read anything more complicated than fairy tales, let alone think well enough to do research.
“Discomfort”: Pain is supposed to be a meaningful signal that something is wrong with the part of the body that hurts. However, with fibromyalgia, things hurt without damage occurring. Pain from fibromyalgia tends to be unresponsive to a wide variety of medications, and one of the best ways of managing the pain is through an exercise regime and visualization. I tend to use the word “discomfort” rather than “pain”, because pain is supposed to be a useful signal of damage, fibromyalgia pain is not useful, and calling it discomfort helps me to try ignore it more effectively.
The clothing thing: The majority of clothing does not work for me any more, because of the feeling of wearing an upset anthill.
The stinging nettle thing: Doing computer work while hands felt like I had been crushing stinging nettles with them.
The bucket thing: Keeping a bucket by my desk while I working, because I was throwing up because of discomfort. Also, avoiding eating before meetings on bad days, so I could make it through the meeting without throwing up into the bucket.
Mobility impairment: This depends on the day. Not a problem at a computer though, so that’s fine, unless on site and the mobility impairment access is garbage (i.e., mostly everywhere).
With sufficient support and accommodations, it is possible to do good science and get a PhD while living with a disability or chronic illness. Dealing with the illness itself is difficult enough, without also having to address barriers that are put in place by people and institutions not working to make things accessible. It’s not that difficult to make things more accessible, and making things more accessible for folks with chronic illness or disabilities also tends to be just good design that makes things more accessible to people without chronic illness or disabilities. Over the next couple of posts I’ll talk about things my lab and university have done to make things more accessible and therefore facilitate my PhD. I’ll also talk about practices that I’m now putting in place for things like seminars I give to make sure that they can reach as many scientists as possible, not just the able-bodied ones.
A few months ago Mick Watson wrote an awesome post about How to recruit a good bioinformatician. We’re in the process of hiring a scientific software engineer so I thought I’d use Mick’s post to illustrate why you should come work with us doing scientific software development and data-intensive research, and hopefully provide a concrete demonstration of the sort of things Mick suggests for appealing to talented computational folks.
Here are Mick’s original suggestions and why I think our position satisfies them.
1. Make sure they have something interesting to do
This is vital. Do you have a really cool research project? Do you have ideas, testable hypotheses, potential new discoveries? Is bioinformatics key to this process and do you recognise that only by integrating bioinformatics into your group will it be possible to realise your scientific vision, to answer those amazing questions?
Software and computational data analysis are core to everything our group does. Just check out our GitHub organization. We’re currently tackling challenging problems in: 1) automatically acquiring and combining heterogenous data; 2) combining large numbers of datasets into single research projects; 3) using machine learning and other computationally intensive modeling approaches to make predictions for ecological systems; and 4) trying to help improve computational and predictive approaches in science more broadly.
2. Make sure they have a good environment to work in
Bioinformatics is unique, I think, in that you can start the day not knowing how to do something, and by the end of the day, be able to do that thing competently. Most bioinformaticians are collaborative and open and willing to help one another. This is fantastic. So a new bioinformatician will want to know: what other bioinformatics groups are around? Is there a journal club? Is there a monthly regional bioinformatics meeting? Are there peers I can talk to, to gain and give help and support?
Or will I be alone in the basement with the servers?
Many members of my group have strong computational and/or machine learning backgrounds (at least for a bunch of scientists). We are also part of the new Informatics Institute at the University of Florida, which is being funded in part through UF’s “Big Data” preeminence initiative. The Informatics Institute brings together faculty, students, and postdocs from across campus with interests in computational science, and the preeminence initiative is recruiting mid-career folks in this area to move to UF (including myself). I’m also a Moore Investigator in Data Driven Discovery and actively involved in the Software Carpentry and Data Carpentry communities, providing strong connections to researchers and developers in all three of these groups. In short, the challenge won’t be finding people to interact with, it will be finding time to interact with all of the different folks you want to talk to.
Speaking of servers, the other type of environment bioinformaticians need is access to good compute resources. Does your institution have HPC? Is there a cluster with enough grunt to get most tasks done? Is there a sys/admin who understands Linux?
Or were you hoping to give them the laptop your student just handed back after having used it during their 4 year PhD? The one with WIndows 2000 on it?
The University of Florida has a brand new high performance computing platform – the HiPerGator. My lab has priority access to a large number of cores on this system. We also have experience working with, and resources to support, AWS and other cloud providers to address our resource needs.
4. Give them a development path
Bioinformaticians love opportunities to learn, both new technical skills and new scientific skills. They work best when they are embedded fully in the research process, are able to have input into study design, are involved throughout data generation and (of course) the data analysis. They want to be allowed to make the discoveries and write the papers. Is this going to be possible? Could you imagine, in your group, a bioinformatician writing a first author paper?
Technical and scientific development is strongly encouraged and supported for all members of our research group. You’ll have time and encouragement to learn new skills, support for travel to training/hackathons/conferences, and active engagement in both the scientific and software development aspects of the lab. Taking the lead on projects and writing first authored papers would be enthusiastically supported for anyone interested in doing so.
5. Pay them what they’re worth
This is perhaps the most controversial, but the laws of supply and demand are at play here. Whenever something is in short supply, the cost of that something goes up. Pay it. If you don’t, someone else will.
We’re doing our best. The position has a top starting salary of $70,000. Thanks to the low cost of living in Gainesville that’s equivalent to about $120,000 in Silicon Valley. We can’t compete with starting salaries for industry, but at least we’re on par with starting salaries for faculty.
6. Drop your standards
Especially true in academia. Does the job description/pay grade demand a PhD? You know what? I don’t have a PhD, and I’m doing OK (group leader for 11 years, over 60 publications, several million in grants won). Take a chance. A PhD isn’t everything
I don’t consider not requiring a PhD to be dropping my standards. We’re looking for the best person whether they have a PhD or not. If you’re good at computers and interested in science, I don’t know what more I could want.
7. Promote them
Got funds for an RA? Try and push it up to post-doc level and emphasize the possibility of being involved in research. Got funds for a post-doc? Try and push it up to a fellowship and offer semi-independence and a small research budget. Got money for a fellowship? Try and push it up to group leader level, and co-supervise a PhD student with them.
This position could have easily been budgeted as a postdoc, but I really wanted to promote the idea of more permanent software developer/engineer positions in academic science. This position is currently funded for 5 years as part of a Moore Foundation Investigator in Data Driven Discovery award. My goal is to make this a permanent position in my group by maintaining long-term funding beyond the 5 years. If I find someone good who wants to stick around I want the salary and responsibility to grow over time (and annual increases in salary are budgeted for the next 5 years).
So, hopefully I’ve done a decent job of satisfying Mick’s requirements. If any of this sounds interesting to you, feel free to leave a comment on this post, drop me an email, chat with me on Twitter, or just go ahead and apply.
My research group is hiring a Scientific Software Engineer to help develop software that facilitates science, contribute to research in data-intensive ecology, and improve scientific research and computing through training and modeling competitions.
We are actively involved in data-intensive computational research, open source software development, and open approaches to science. The engineer will work as part of a collaborative group, including undergraduates, graduate students and postdocs, using large amounts of ecological and environmental data to understand natural systems. They will develop and maintain open source software designed for working with large amounts of heterogeneous data, collaborate on research projects making predictions for ecological systems, and help develop web infrastructure for scientists to share, evaluate and improve predictions. In doing so they will actively interact with, and contribute to, related efforts from other initiatives and projects in these areas (e.g., rOpenSci, Dat, Software Carpentry, Data Carpentry, DataONE, NCEAS).
Are you a software developer who’s interested in science? Great! Are you a scientist with strong software skills? Awesome! If you have some experience with Python or R, Git, database management systems, web development, spatial data, and/or PostgreSQL/PostGIS, we’d be excited, but what we’re really interested in is someone who is good with computers, interested in science, enjoys working on a variety of projects, likes learning new tools as needed, and works well in a diverse team.
The University of Florida is a great place to work in the computational, data-intensive, and informatics side of science. They have a major hiring initiative in “big data”, a new Informatics Institute that we are a part of, and a top notch Research Computing Center (aka HPC). In addition, I am a Moore Foundation Investigator in Data-Driven Discovery and actively engaged in the computational and data-intensive science communities. This makes my lab a good place to work if you enjoy that sort of thing (checkout our GitHub organization if you want to see what we’ve been up to recently). We also work hard to provide a positive and supportive environment that treats all members of the group as important contributors and actively values diversity.
This position has guaranteed support for the next five years. My goal is for this to be a long-term position in our research group and a model for similar positions in other research groups.
If you’ve made it this far you might be interested in a few more details of the projects this position might be involved in. These include:
- Developing, maintaining, and providing support for open source software for acquiring, cleaning, combining, and managing large numbers of heterogeneous datasets. This will include Python based development and maintenance of the EcoData Retriever software and the development of new software to automatically combine multiple datasets together for analysis.
Working in collaborative teams to conduct scientific research including the use of machine learning for making predictions and forecasts for ecological systems.
Developing, maintaining, and providing support for an open source system for publicly sharing ecological predictions and forecasts and automatically evaluating those predictions as new data is released. This system will be designed to allow researchers to collaborate and compete to improve predictions by uploading predictions to be compared to test data and/or by uploading code to make predictions.
Engaging with the broader community of projects involved in acquiring, cleaning, and combining heterogeneous datasets (e.g., rOpenSci, DataONE, dat), as well as those training scientists in the use of data and computation (e.g., Software Carpentry, Data Carpentry). This includes contributing to open source and participating in related conferences and hackathons.
To apply please visit the official University of Florida job ad. If you have any questions feel free to leave a comment on this post, drop me an email, chat with me on Twitter, or check this blog later in the week to find out why I think this will be a pretty rewarding job. You can also check out our websites to find out more about my lab and our interdisciplinary research group.
UPDATE: Here’s the post I promised on why this will hopefully be a rewarding job.
The newly created Early Career Ecologist Section of the Ecological Society of America is to organizing a mentoring program for the upcoming ESA meeting in Baltimore. ESA can be a big and intimidating meeting for students and postdocs. Let’s face it, many of us are socially awkward and meeting new people can be hard – especially if the people you want to meet are more senior. The aim of the mentoring program is to help overcome that social activation energy by pairing each mentee with a more senior scientist who will happily interact with you on research and career issues and (hopefully) help introduce you around to other people you might be interested in meeting with. The program is open to all career paths and ecological fields and is open to senior graduate students (~1 year from graduation) and early postdocs (< 4 years postdoc experience). More info on the program and applying for it can be found here
Oh, and there is financial support to help defray registration costs for the mentees in the program.
And you more senior scientists? The section is also looking for mentors. If you are interested in being involved as a mentor (I know I am), then contact the section and volunteer! I know they are hoping to get people from a diverse array of job types (government, NGOs, academics, consulting firms, whatever) representing a diverse array of racial and gender role models. They are looking to create as diverse a mentor pool as possible to give the mentees a wealth of choices to pick from. IF you want to know more about the mentor side of things, more info can be found here
The above links (plus additional info) can also be found on the Early Career Sections website, under “Centennial Mentoring Program” header.
You can also contact the Early Career Ecologist Section directly via twitter (@esa_earlycareer) or email (email@example.com)
The deadline for applying is: 8 pm PST on Feb 28 2015
A couple of months ago Micah J. Marty and I had a twitter conversation and subsequent email exchange about how citations worked with preprints. I asked Micah if I could share our email discussion since I thought it would be useful to others and he kindly said yes. What follows are Michah’s questions followed by my responses.
Right now, I am finishing up a multi-chapter Master’s thesis and I plan to publish a few papers from my work. I may want to submit a preprint of one manuscript but before I propose this avenue to my advisor, I want to understand it fully myself. And I have remaining questions about the syntax of citing works when preprints come into play. What happens to a citation of a preprint after the manuscript is later published in a peer reviewed venue?
At the level of the journal nothing happens. So, if you cite a preprint in a published ms, and that preprint is later published as a paper, then the citation is still to the preprint. However, some of the services indexing citations recognize the relationship between the preprint and the paper and aggregate the citations. Specifically, Google Scholar treats the preprint and the published paper as the same for citation analysis purposes. See the citation record for our paper on Best practices for scientific computing which has been cited 49 times, but the vast majority of those are citations to the preprints.
Here’s an example with names we can play with: Manuscript 1 (M1) may require some extra analysis, but it presents some important unexpected results that I would like to get out on the table as soon as possible. M1 is submitted to PeerJ Preprints and accepted (i.e., published online as a preprint with a DOI). M2 is submitted to Marine Ecology Progress Series (MEPS) for peer review, and M2 cites the PeerJ Preprint M1.
Just a point related to vocabulary, I wouldn’t typically think of the preprint as being “accepted”. Any checking prior to posting is just a quick glance to make sure that it isn’t embarrassingly bad, so as long as it’s reasonably written and doesn’t have a title like “E is not equal to mc squared” it will be posted almost immediately (within 48 hours on most preprint servers).
1) Are preprints considered “grey literature”? That is, is it illegitimate for M2 to cite a work that has not been peer reviewed?
Yes, in the sense that they haven’t been formally peer reviewed prior to posting they are similar to “grey literature”. Whether or not they can be cited depends on the journal. Some journals are happy to allow citing of preprints. For example, this recent paper in TREE cites a preprint of ours on arXiv. Their paper was published before ours was accepted, so if it wasn’t for the preprint it couldn’t have been cited.
2) Is there a problem if M1 is eventually published in a peer reviewed journal but the published article of M2 cites only the PeerJ Preprint of M1?
I would say no for two reasons. First, assuming that M2 is published before M1 then the choice is between having a citation to something that people can read, science can benefit from, and that can potentially be indexed (giving you citation credit) vs. a citation to “Marty et al. unpublished data”, which basically does nothing. Second, all preprint servers provide a mechanism for linking to the final version, so if someone finds the preprint via a citation in M2 then that link will point them in the direction of the final version that they can then read/cite/etc.
In short, I think as long as you aren’t planning on submitting to a behind the times journal that doesn’t allow the submission of papers that have been posted as preprints (and the list of journals with this policy is shrinking rapidly) then there is no downside to posting preprints. In the best case scenario it can lead to more people reading your research and citing it. The worst case scenario is exactly the same as if you didn’t post a preprint.
I am currently the remotely working member of Weecology, finishing up my PhD in the lower elevation and better air of Kansas, while the rest of my colleagues are still in Utah, due to developing a chronic illness and finally getting diagnosed with fibromyalgia. The relocation is actually working out really well. I’m in better shape because I’m not having to fight the air too, and I’m finally making real progress toward finishing my dissertation again.
I ruthlessly culled everything that wasn’t directly working on my dissertation. I was going to attend the Gordon Conference this year, as I had heard fantastic things about it for years, but had not been ready to go yet, but I had to drop that because I wasn’t physically able to travel. I did not go to ESA, because I couldn’t travel. There are working groups and workshops galore, all involving travel, which I cannot do. Right now, the closest thing that we have to bringing absent scientists to an event is live tweeting, which is not nearly as good as hearing a speaker for yourself, and is pretty heartbreaking if you had to cancel your plans to attend an event because you were too infirm to go.The tools that I’m using to do science remotely are not just for increasing accessibility for a single chronically ill macroecologist. They are good tools for science in general. I’m using GitHub to version control my code, and Dropbox to share data and figures. Ethan can see what I’m working on as I’m doing it, and I’ve got a clear record of what I was doing and what decisions that I made. While my cognitive dysfunction may be a bit more extreme of a problem, I know that we’ve all stayed up too late coding and broken something we shouldn’t have and the ability to wave the magic Git wand and make any poor decisions that I made while my brain was out to lunch go away is priceless.
Open access? Having open access to papers is really important when you are going to be faced shortly with probably not having any institutional access anymore. Also, important for everyone else who isn’t at a major university with very expensive subscriptions to all the journals. Having open access to data and code is crucial when you can’t collect your own data and are going to be doing research from your home computer on the cheap because you can’t rely on your body to work reliably at any given point in time.
Video conferencing is working well for me to meet with the lab, but could also be great for attending conferences and workshops. This would not only be good for a certain macroecologist, but would also be good to include people from smaller universities, etc. who would like to participate in these type of things too, but can’t otherwise due to the travel. I did my master’s degree at Fort Hays State University, and I still love it dearly. This type of increased accessibility would have been great for me while I was a perfectly healthy master’s student. Fort Hays is a primarily undergraduate institution in the middle of Kansas, about four hours away from any major city, and it does not have some of the resources that a larger university would have. No seminar series, no workshops, not much travel money to go to workshops or conferences, which doesn’t mean that good science can’t still be happening.
Many of my labmates are looking for post-docs, or are already in postdoc positions at this point. I’m very excited for all of them, and await eagerly all the stories of the exciting new things they are doing. Having a chronic illness limits what I am capable of doing physically. I am not going to be able to move across the country for a post-doc. That does not mean that I do not want to play science too. I’ve got my home base set up, and I can reach pretty far from here. I still want to be a part of living science, I don’t want to have to get to the party after everyone else has gone home.
And I wonder, why can I not do these things? Is it not the future? Do we not have the internet, with video chat? I get to meet with Ethan and talk science at our weekly meetings every week. I go to lab meetings with video chat, and get to see what my labmates are doing, and crack jokes, and laugh at other people’s jokes. It wouldn’t be hard to get me to conferences and working groups either.
With technology, I get to be a part of living, breathing science, and it is a beautiful thing.
I’m looking for one or more graduate students to join my group next fall. In addition to the official add (below) I’d like to add a few extra thoughts. As Morgan Ernest noted in her recent ad, we have a relatively unique setup at Weecology in that we interact actively with members of the Ernest Lab. We share space, have joint lab meetings, and generally maintain a very close intellectual relationship. We do this with the goal of breaking down the barriers between the quantitative side of ecology and the field/lab side of ecology. Our goal is to train scientists who span these barriers in a way that allows them to tackle interesting and important questions.
I also believe it’s important to train students for multiple potential career paths. Members of my lab have gone on to faculty positions, postdocs, and jobs in both science non-profits and the software industry.
Scientists in my group regularly both write papers (e.g., these recent papers from dissertation chapters: Locey & White 2013, Xiao et al. 2014) and develop or contribute to software (e.g., EcoData Retriever, ecoretriever, rpartitions & pypartitions) even if they’ve never coded before they joined my lab.
My group generally works on problems at the population, community, and ecosystem levels of ecology. You can find out more about what we’ve been up to by checking out our website. If you’re interested in learning more about where the lab is headed I recommend reading my recently funded Moore Investigator in Data-Driven Discovery proposal.
PH.D STUDENT OPENINGS IN QUANTITATIVE, COMPUTATIONAL, AND MACRO- ECOLOGY
The White Lab at the University of Florida has openings for one or more PhD students in quantitative, computational, and/or macro- ecology to start fall 2015. The student(s) will be supported as graduate research assistants from a combination of NSF, Moore Foundation, and University of Florida sources depending on their research interests.
The White Lab uses computational, mathematical, and advanced statistical/machine learning methods to understand and make predictions/forecasts for ecological systems using large amounts of data. Background in quantitative and computational techniques is not necessary, only an interest in learning and applying them. Students are encouraged to develop their own research projects related to their interests.
The White Lab is currently at Utah State University, but is moving to the Department of Wildlife Ecology and Conservation at the University of Florida starting summer 2015.
Interested students should contact Dr. Ethan White (firstname.lastname@example.org) by Nov 15th, 2014 with their CV, GRE scores, and a brief statement of research interests.
UPDATE: Added a note that we work at population, community, and ecosystem levels.
We are very excited to announce the newest release of our EcoData Retriever software and the first release of a supporting R package, ecoretriever. If you’re not familiar with the EcoData Retriever you can read more here.
The biggest improvement to the Retriever in this set of releases is the ability to run it directly from R. Dan McGlinn did a great job leading the development of this package and we got ton of fantastic help from the folks at rOpenSci (most notably Scott Chamberlain, Gavin Simpson, and Karthik Ram). Now, once you install the main EcoData Retriever, you can run it from inside R by doing things like:
install.packages('ecoretriever') library(ecoretriever) # List the datasets available via the Retriever ecoretriever::datasets() # Install the Gentry dataset into csv files in your working directory ecoretriever::install('Gentry', 'csv') # Download the raw Gentry dataset files, without any processing, # to the subdirectory named data ecoretriever::download('Gentry', './data/') # Install and load a dataset as a list Gentry = ecoretriever::fetch('Gentry') names(Gentry) head(Gentry$counts)
The other big advance in this release is the ability to have the Retriever directly download files instead of processing them. This allows us to support data that doesn’t come in standard tabular forms. So, we can now include things like environmental data in GIS formats and phylogenetic data such as supertrees. We’ve used this new capability to allow the automatic downloading of the Bioclim data, one of the most widely used climate datasets in ecology, and the supertree for mammals from Fritz et al. 2009.
I am incredibly excited to announce that I am the recipient of one of the Moore Foundation’s Investigators in Data-Driven Discovery awards.
To quote Chris Mentzel, the Program Director of the Data-Driven Discovery Initiative:
Science is generating data at unprecedented volume, variety and velocity, but many areas of science don’t reward the kind of expertise needed to capitalize on this explosion of information. We are proud to recognize these outstanding scientists, and we hope these awards will help cultivate a new type of researcher and accelerate the use of interdisciplinary, data-driven science in academia.
I feel truly honored to have been selected. All the finalists that I met at the Moore Foundation in July were amazing as were all of the semi-finalists that I knew. I did not envy the folks making the final decisions.
So what will we be doing with this generous support from the Moore Foundation?
- Doing data-intensive prediction and forecasting in ecological systems: We’ll be focusing on population and community level forecasting as well as ecosystem level work where it interfaces with community level approaches. We’ll be using both process based ecological approaches with machine learning, with an emphasis on developing testable predictions and evaluating them with independent (out-of-sample) data. As part of this effort we’ll be making publicly available forecasts for large ecological datasets prior to the collection of the next round of data, following Brian McGill’s 6th P of Good Prediction (in fact we’ll be trying to follow all of his P’s as much as possible). There’s a lot of good work in this area and we’ll be building on it rather than reinventing any wheels.
- Increasing the emphasis on testable prediction and forecasting in ecology more broadly: Industry and other areas of science have improved their prediction/forecasting through competitions that provide data with held out values and challenge folks to see who can do the best job of predicting those values (most notable in Kaggle competitions). We’ll be helping put together something like this for ecology and hopefully integrating that with our advanced predictions to allow other folks to easily make predictions from their models public and have them evaluated automatically as new data is released.
- Tools for making data-intensive approaches to ecology easier: We’ll be continuing our efforts to make acquiring and working with ecological data easier. Our next big step is to make combining numerous ecological and environmental datasets easy so that researchers can focus on doing science rather than assembling data.
- Training: We’ll be helping build and grow Data Carpentry, a new training effort that is a sister project to Software Carpentry with a focus on data management, manipulation and analysis.
I’m very excited to be joined in this honor by my open science/computational training/data-intensive partner in crime C. Titus Brown(@ctitusbrown). I was also particularly thrilled to find out that I wasn’t the only investigator studying ecological systems. Laurel Larsen is in the Geography department at Berkeley and I can’t wait to interact with her more as we both leverage large amounts of ecological data to improve our understanding of ecological systems and our ability to forecast their states in the future. We are joined by astronomers, statisticians, computer scientists, and more. Check out the entire amazing group at the official Moore Foundation Investigators site and see the full press release for additional details about the program.
The award is being run through the University of Florida since we are in the process of relocating there, but I owe a huge dept of gratitude to the Biology Department and the Ecology Center at Utah State University for always supporting me while I spent time developing software, working on computational training initiatives, and generally building a data-intensive ecology program. Without their support I have no doubt that I wouldn’t be writing this blog post today.
So here it is, the first of the positions we’ll be advertizing as part of our move to the University of Florida. The official ad is below, but a few comments first. The position is for a student to work with me, but for those who aren’t really familiar with our groups, it’s important to note that my group works closely with Ethan White’s lab (we provide desk space that mixes the labs together, we have a single group lab meeting, etc). My group tends to attract people who like to do field work. Ethan’s tends to attract people who are more quantitatively or computationally inclined. We mix our groups because we believe that the divide that exists between quantitative and field-based approaches to ecology is bad for our science and that we need more people trained to serve as bridges between the quantitative and field-oriented worlds of ecology.
Here are some links to the papers my students have published from their dissertations to get a feel for what my students have intellectually gotten out of this environment:
PH.D STUDENT OPENING IN COMMUNITY ECOLOGY
The Ernest Lab at the University of Florida has an opening for a Ph.D student in the area of Community Ecology to start fall 2015. The student will be supported as a graduate research assistant as part of an NSF-funded project at a long-term research site (portalproject.weecology.org) in southeastern Arizona to study regime shifts (rapid shifts in ecosystem structure and function). This position will participate in data collection efforts in Arizona on rodents and plants.
The Ernest lab is interested in general questions about the processes that structure communities, with a particular focus on understanding how ecological communities change through time. Students are free to develop their own research projects depending on their interests.
The Ernest Lab is currently at Utah State University, but is moving to the Department of Wildlife Ecology and Conservation at the University of Florida starting summer 2015.
More information about the lab is available at: http://ernestlab.weecology.org
Interested students should contact Dr. Morgan Ernest (email@example.com) by Nov 15th, 2014 with their CV, GRE scores, and a brief statement of research interests.