As a budding macroecologist, I have thought a lot about what skills I need to acquire during my Ph.D. This is my model of the four basic attributes for a macroecologist, although I think it is more generally applicable to many ecologists as well:
- Knowledge of SQL
- Dealing with proper database format and structure
- Finding data
- Appropriate treatments of data
- Understanding what good data are
- Monte Carlo methods
- Maximum likelihood methods
- Power analysis
- Higher level calculus
- Should be able to derive analytical solutions for problems
- Should be able to write programs for analysis, not just simple statistics and simple graphs.
- Able to use version control
- Once you can program in one language, you should be able to program in other languages without much effort, but should be fluent in at least one language.
Achieve expertise in at least 2 out of the 4 basic areas, but be able to communicate with people who have skills in the other areas. However, if you are good at collaboration and come up with really good questions, you can make up for skill deficiencies by collaborating with others who possess those skills. Start with smaller collaborations with the people in your lab, then expand outside your lab or increase the number of collaborators as your collaboration skills improve.
Achieving proficiency in an area is best done by using it for a project that you are interested in. The more you struggle with something, the better you understand it eventually, so working on a project is a better way to learn than trying to learn by completing exercises.
The attribute should be generalizable to other problems: For example, if you need to learn maximum likelihood for your project, you should understand how to apply it to other questions. If you need to run an SQL query to get data from one database, you should understand how to write an SQL query to get data from a different database.
In graduate school:
Someone who wants to compile their own data or work with existing data sets needs to develop a good intuitive feel for data; even if they cannot write SQL code, they need to understand what good and bad databases look like and develop a good sense for questionable data, and how known issues with data could affect the appropriateness of data for a given question. The data skill is also useful if a student is collecting field data, because a little bit of thought before data collection goes a long way toward preventing problems later on.
A student who is getting a terminal master’s and is planning on using pre-existing data should probably be focusing on the data skill (because data is a highly marketable skill, and understanding data prevents major mistakes). If the data are not coming from a central database, like the BBS, where the quality of the data is known, additional time will have to be added for time to compile data, time to clean the data, and time to figure out if the data can be used responsibly, and time to fill holes in the data.
Master’s students who want to go on for a Ph.D. should decide what questions they are interested in and should try to pick a project that focuses on learning a good skill that will give them a headstart- more empirical (programming or stats), more theoretical (math), more applied (math (e.g., for developing models), stats(e.g., applying pre-existing models and evaluating models, etc.), or programming (e.g. making tools for people to use)).
Ph.D. students need to figure out what types of questions they are interested in, and learn those skills that will allow them to answer those questions. Don’t learn a skill because it is trendy or you think it will help you get a job later if you don’t actually want to use that skill. Conversely, don’t shy away from learning a skill if it is essential for you to pursue the questions you are interested in.
Right now, as a Ph.D. student, I am specializing in data and programming. I speak enough math and stats that I can communicate with other scientists and learn the specific analytical techniques I need for a given project. For my interests (testing questions with large datasets), I think that by the time I am done with my Ph.D., I will have the skills I need to be fairly independent with my research.
There is an exciting postdoc opportunity for folks interested in quantitative approaches to studying evolution in Michael Gilchrist’s lab at the University of Tennessee. I knew Mike when we were both in New Mexico. He’s really sharp, a nice guy, and a very patient teacher. He taught me all about likelihood and numerical maximization and opened my mind to a whole new way of modeling biological systems. This will definitely be a great postdoc for the right person, especially since NIMBioS is at UTK as well. Here’s the ad:
Outstanding, motivated candidates are being sought for a post-doctoral position in the Gilchrist lab in the Department of Ecology & Evolutionary Biology at the University of Tennessee, Knoxville. The successful candidate will be supported by a three year NSF grant whose goal is to develop, integrate and test mathematical models of protein translation and sequence evolution using available genomic sequence and expression level datasets. Publications directly related to this work include Gilchrist. M.A. 2007, Molec. Bio. & Evol. (http://www.tinyurl/shahgilchrist11) and Shah, P. and M.A. Gilchrist 2011, PNAS (http://www.tinyurl/gilchrist07a).
The emphasis of the laboratory is focused on using biologically motivated models to analyze complex, heterogeneous datasets to answer biologically motivated questions. The research associated with this position draws upon a wide range of scientiﬁc disciplines including: cellular biology, evolutionary theory, statistical physics, protein folding, diﬀerential equations, and probability. Consequently, the ideal candidate would have a Ph.D. in either biology, mathematics, physics, computer science, engineering, or statistics with a background and interest in at least one of the other areas.
The researcher will collaborate closely with the PIs (Drs. Michael Gilchrist and Russell Zaretzki) on this project but potentiall have time to collaborate on other research projects with the PIs. In addition, the researcher will have opportunties to interact with other faculty members in the Division of Biology as well as researchers at the National Institute for Mathematical and Biological Synthesis (http://www.nimbios.org).
Review of applications begins immediately and will continue until the position is filled. To apply, please submit curriculum vitae including three references, a brief statement of research background and interests, and 1-3 relevant manuscripts to mikeg[at]utk[dot]edu.
There is a new postdoctoral research position available in Jim Brown’s lab at the University of New Mexico to study some of the major patterns of biodiversity. We know a bit about the research and it’s going to be an awesome project with a bunch of incredibly bright people involved. Jim’s lab is also one of the most intellectually stimulating and supportive environments that you could possibly work in. Seriously, if you are even remotely qualified then you should apply for this position. We’re both thinking about applying and we already have faculty positions :). Here’s the full ad:
The Department of Biology at the University of New Mexico is seeking applications for a post-doc position in ecology/biodiversity. The post doc will be expected to play a major role in a multi-investigator, multi- institutional project supported by a four-year NSF Macrosystems Ecology grant. The research will focus on metabolic processes underlying the major patterns of biodiversity, especially in pervasive temperature dependence and requires a demonstrated working knowledge of theory, mathematical and computer
Applicants must have a Ph.D. in ecology or a related discipline.
Review begins with the first applications and continues until the position is filled. Applicants must submit a cover letter and a curriculum vitae along with at least three phone numbers of references, three letters of recommendation and PDF’s of relevant preprints and publications to be sent directly to firstname.lastname@example.org attn: James Brown. Application materials must be received by July 25, 2011, for best consideration.
Questions related to this posting may be directed to Dr. James Brown at email@example.com or to Katherine Thannisch at firstname.lastname@example.org.
The University of New Mexico is an Equal Opportunity/Affirmative Action Employer and Educator. Women and underrepresented minorities are encouraged to apply.
Advertisements for three exciting postdoctoral positions came out in the last week.
Interface between ecology, evolution and mathematics
The first is with Hélène Morlon’s group in Paris. Hélène and I were postdocs in Jessica Green’s lab at the same time. She is both very smart and extremely nice, oh, and did I mention, her lab is in PARIS. Here’s the ad. If it’s a good fit then you couldn’t go wrong with this postdoc.
A postdoctoral position is available in my new lab at the Ecole Polytechnique and/or at the Museum of Natural History in Paris to work at the interface between ecology-evolution and mathematics. Candidates with a background in biology and a strong interest in modeling, or with a theoretical background and a strong interest in biology, are encouraged to apply. More information is available here. Potential candidates should feel free to contact me. The deadline for application is May 8th.
The other two postdocs are associated with Tim Keitt’s lab (which I consider to be one of the top quantitative ecology groups out there).
Mechanistic niche modeling and climate change impacts
A postdoctoral position is anticipated as part of a collaborative project to develop and evaluate mechanistic niche models that incorporate geographic variation in physiological traits. The post doc will be based in Michael Angilletta’s laboratory at Arizona State University, but will interact with members of Lauren Buckley’s lab at the University of North Carolina in Chapel Hill and Tim Keitt’s lab at the University of Texas in Austin. The post doc will be expected to engage in modeling activities and coordinate lab studies of thermal physiology. Experience with mathematical modeling in C++, MATLAB, Python or R is beneficial and familiarity with environmental data and biophysical ecology is beneficial. More here.
Ecological forecasting or statistical landscape genetics
The Keitt Lab at the University of Texas at Austin seeks a postdoctoral investigator to join an interdisciplinary NSF-funded project linking ecophysiology, genomics and climate change. The position requires excellent modeling skills and the ability to engage in multidisciplinary research. Research areas of interest include either ecological forecasting or statistical landscape genomics. More here.
So, if you’re looking for a job go check out these great opportunities.
We have a postdoc position available for someone interested in the general areas of macroecology, quantitative ecology, and ecoinformatics. Here’s the short ad with links to the full job description:
Ethan White’s lab at Utah State University is looking for a postdoc to collaborate on research studying approaches for unifying macroecological patterns (e.g., species abundance distributions and species-area relationships) and predicting variation in these patterns using ecological and environmental variables. The project aims to 1) evaluate the performance of models that link ecological patterns by using broad scale data on at least three major taxonomic groups (birds, plants, and mammals); and 2) combine models with ecological and environmental factors to explain continental scale variation in community structure. Models to be explored include maximum entropy models, neutral models, fractal based models, and statistical models. The postdoc will also be involved in an ecoinformatics initiative developing tools to facilitate the use of existing ecological data. There will be ample opportunity for independent and collaborative research in related areas of macroecology, community ecology, theoretical ecology, and ecoinformatics. The postdoc will benefit from interactions with researchers in Dr. White’s lab, the Weecology Interdisciplinary Research Group, and with Dr. John Harte’s lab at the University of California Berkeley. Applicants from a variety of backgrounds including ecology, mathematics, statistics, physics and computer science are encouraged to apply. The position is available for 1 year with the possibility for renewal depending on performance, and could begin as early as September 2010 and no later than May 2011. Applications will begin to be considered starting on September 1, 2010. Go to the USU job page to see the full advertisement and to apply.
If you’re interested in the position and are planning to be at ESA please leave a comment or drop me an email (email@example.com) and we can try to set up a time to talk while we’re in Pittsburgh. Questions about the position and expressions of interest are also welcome.
UPDATE: This position has been filled.
Imperial College London is offering a new masters degree program in quantitative biology. It sounds like a great opportunity to get some good quantitative training via an intensive 1 year MS program. The best part of their pitch follows below. If you’d like to see the whole ad check out the flier that Dan Reuman sent me.
Over the past 10-20 years, biology has become increasingly quantitative, and mathematical sciences have in turn been increasingly influenced by biology. It has been said that “mathematics is biology’s next microscope, only better” (Cohen, J.E., PloS Biology, 2004) because mathematical, statistical, and computational sciences will continue to reveal unsuspected and entirely new worlds within biology, just as the microscope revealed previously unseen worlds following its invention. It has also been said that “biology is mathematics’ next physics, only better” (Cohen, J.E., PloS Biology, 2004) because biology will in turn continue to spur major new developments in computation, mathematics and statistics, just as physics has done in the past several hundred years.
Recognizing this integration, the MSc in Quantitative Biology provides students of life sciences with the quantitative skills they will need to thrive in the modern discipline of biology, and provides students from a more quantitative background with the biological insight they need to apply their technical skills. The course is unique in integrating important current research questions in biology with data from ecosystems down to cells and state-of-the-art quantitative methods. Graduates will be highly trained scientists prepared for employment in any of several settings, including as PhD students in universities and institutes worldwide; in the research departments of multinational industries concerned with the environment (e.g., pharmaceuticals, biotechnology); in conservation, management and agricultural agencies; and in local and national governments.
I went to graduate school with Ford and would strongly recommend that those looking for PhD opportunities on the quantitative side of ecosystem ecology consider the opportunity below. Ford is a smart guy, doing cool work, and he knows an awful lot about math, so it’s probably pretty hard to go wrong (and yes, we’re still friends so I’m totally biased).
The Ballantyne Lab at the University of Kansas is looking to recruit up to two graduate students for the fall of 2010. Current research is focused on modeling ecosystem stoichiometry, nutrient dynamics, microbial decomposition of soil carbon, systems-level regulation of metabolism, spatially explicit populations and the trophic structure of communities. Although most of our experiments are performed with phytoplankton and bacteria in the lab, the KU field station, 20 minutes from campus, is a great resource that is home to long-term studies of community assembly. Please direct inquiries to Ford Ballantyne (fb4 [at] ku [dot] edu). For more information about graduate study in the lab and EEB at KU please look at http://www.people.ku.edu/~fb4 and http://www2.ku.edu/%7Eeeb/graduate/ and http://www.kuerg.ku.edu/.