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<p style="width: 200px;" align="right"><font size="5"><a href="index.html">Home</a></font>
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<td style="vertical-align: top; width: 80%;"><font style="color: rgb(0, 102, 0);" size="+3">Resources for PhD students in
AI/ML</font><br>
<br>
<font size="+1"><span style="font-weight: bold;">Getting started<br>
</span></font>
<ul>
<li><font size="+1">Why PhD at all? <a href="http://www.fast.ai/2018/08/27/grad-school/">Before considering
PhD</a> | <a href="http://archive.cra.org/reports/why.cs.phd.pdf">A CS
PhD</a>? <a href="http://www.businessinsider.com.au/the-illustrated-guide-to-a-phd-2012-3">PhD
as pushing knowledge boundary</a></font></li>
<li><font size="+1"><a href="http://phdcomics.com/comics.php">PhD
Comics</a></font></li>
<li><font size="+1"><a href="https://truyentran.github.io/scholarship.html">Scholarhips at A<sup>2</sup>I<sup>2</sup></a>.</font></li>
</ul>
<font size="+1"><span style="font-weight: bold;">Advices (CS)</span><br>
</font>
<ul>
<li><font size="+1"><a style="font-weight: bold;" href="http://karpathy.github.io/2016/09/07/phd/">A Survival Guide to a
PhD</a> by Andrej Karpathy</font></li>
<li><font size="+1"><a style="font-weight: bold;" href="https://github.com/jbhuang0604/awesome-tips">Tips for PhD</a> collected by Jia-Bin Huang<br>
</font></li>
<li><font size="+1"><a style="font-weight: bold;" href="https://ruder.io/10-tips-for-research-and-a-phd/">10 Tips for
Research and a PhD</a> by Sebastian Ruder<br>
</font></li>
<li><font size="+1"><a style="font-weight: bold;" href="http://joschu.net/blog/opinionated-guide-ml-research.html">An
Opinionated Guide to ML Research</a> by John Schulman<br>
</font></li>
<li><font size="+1"><a style="font-weight: bold;" href="https://medium.com/s/story/so-you-want-to-be-a-research-scientist-363c075d3d4c">So
you want to be a research scientist</a> by Vincent Vanhoucke, aka, <span style="font-style: italic;">how to do modern ML/AI research</span><br>
</font></li>
<li><font size="+1"><a href="http://www.pgbovine.net/early-stage-PhD-advice.htm">Advice for
early-stage Ph.D. students</a> by Philip Guo</font></li>
<li><font size="+1"><a href="http://www.taoli.ece.ufl.edu/GoodCareer.pdf">How to have a good
CS career</a> by Stefan Savage<br>
</font></li>
<li><font size="+1"><a href="http://www.cs.indiana.edu/mit.research.how.to.html">How to do
Research At the MIT AI Lab</a></font></li>
<li><font size="+1"><a href="http://www.cs.ucr.edu/%7Eeamonn/Keogh_SIGKDD09_tutorial.pdf">How
to do good (data mining) research, get it published</a> by Eamonn
Keogh</font></li>
<li><font size="+1"><a href="http://www.cs.indiana.edu/how.2b/how.2b.html">How to Be a Good
Graduate Student</a> by Marie desJardins<a href="http://www.csee.umbc.edu/%7Emariedj/"><span style="color: rgb(0, 0, 0); font-family: 'Times New Roman'; font-size: medium; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 1; word-spacing: 0px; display: inline ! important; float: none;"></span></a></font></li>
<li><font size="+1"><a href="http://homes.cs.washington.edu/%7Emernst/advice/">Advice for
researchers and students</a>, compiled by Michael Ernst</font></li>
</ul>
<font size="+1"><span style="font-weight: bold;">Thoughts on curent AI research<br>
</span></font>
<ul>
<li>How to deal with a fast changing landscape<br>
</li>
<li><font size="+1"><a href="https://www.jasonwei.net/blog/ai-research-is-a-max-performance-domain">AI research is a max-performance domain</a> by Jason Wei.</font></li>
<li><font size="+1"><a href="https://x.com/_jasonwei/status/1791192069022810444">On ambition at junior level</a> by Jason Wei</font></li>
<li><font size="+1"><a href="https://x.com/_jasonwei/status/1861496796314493376">AI move onto science and engineering</a> & <a href="https://x.com/_jasonwei/status/1907532120446349353">two styles to do so</a> by Jason Wei</font></li>
<li><font size="+1"><a href="https://x.com/_jasonwei/status/1929621539881996607">Model-driven vs method-driven research</a> by Jason Wei</font></li>
<li><font size="+1"><a href="https://x.com/_jasonwei/status/1929986444262813701">The reverse 20-80 rule</a> by Jason Wei<br>
</font></li>
</ul>
<font size="+1"><span style="font-weight: bold;">Background on AI
& Machine learning</span></font><br>
<ul>
<li><font size="+1">Maths books</font></li>
<ul>
<li><font size="+1"><a href="https://www.amazon.com/dp/0030105676/"><span style="font-style: italic;">Linear algebra and its applications</span></a>
by Gilbert Strang.</font></li>
<li><font size="+1"><a href="https://www.amazon.com/All-Statistics-Statistical-Inference-Springer/dp/1441923225"><span style="font-style: italic;">All of statistics</span></a> by Larry
Wasserman, 2010.</font></li>
<li><font size="+1"><a href="https://web.stanford.edu/%7Eboyd/cvxbook/"><span style="font-style: italic;">Convex optimization</span></a> by Stephen
Boyd and Lieven Vandenberghe.<br>
</font></li>
</ul>
<li><font size="+1">AI books</font></li>
<ul>
<li><font size="+1"><a style="font-style: italic;" href="http://aima.cs.berkeley.edu/">AI: A modern approach</a> by S.
Russell and P. Norvig, 4rd ed 2020.</font></li></ul>
<li><font size="+1">ML books</font></li>
<ul>
<li><font size="+1"><a href="http://statweb.stanford.edu/%7Etibs/ElemStatLearn/"><span style="font-style: italic;">The elements of statistical learning</span></a>
by Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2nd ed,
2009.</font></li>
<li><font size="+1">Pattern Recognition and Machine Learning<br>
by Christopher M. Bishop, 2006.<br>
</font></li>
<li><font size="+1"><a href="https://probml.github.io/pml-book/"><span style="font-style: italic;">Machine Learning: a Probabilistic
Perspective</span></a> by Kevin Murphy, 2022-2023.</font></li>
<ul>
<li><font size="+1">Similar book: <a style="font-style: italic;" href="http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online">Bayesian
Reasoning and Machine Learning</a> by David Barber, 2017.<br>
</font></li>
</ul>
<li><font size="+1"><a href="http://incompleteideas.net/book/the-book-2nd.html"><span style="font-style: italic;">Reinfocement learning: An introduction</span></a>
by Sutton and Barto, 2018<br>
</font></li>
</ul>
<li><font size="+1">Deep learning books</font></li>
<ul>
<li> <font size="+1"><a style="font-style: italic;" href="http://www.deeplearningbook.org/"></a><a style="font-style: italic;" href="https://www.bishopbook.com/">Deep Learning: Foundations and Concepts</a> by C. Bishop & H. Bishop, 2023.</font></li>
<li><font size="+1"><a style="font-style: italic;" href="https://www.manning.com/books/deep-learning-with-python-second-edition">Deep Learning with Python</a> by Francois Chollet, 2021.<br>
</font></li>
<li><font style="font-style: italic;" size="+1"> <a href="http://d2l.ai/index.html">Dive into Deep Learning</a></font><font size="+1"> by Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J.,
2021.</font></li>
</ul>
<li><font size="+1">CV books</font></li>
<ul><li><font size="+1"><a href="https://szeliski.org/Book/"><span style="font-style: italic; text-decoration: underline;">Computer
Vision: Algorithms and Applications</span></a> </font><font size="+1">by
Richard Szeliski,</font><font size="+1"> 2022.</font></li>
</ul>
<li><font size="+1">NLP books</font></li>
<ul>
<li><font size="+1"><a href="https://mitpress.mit.edu/books/introduction-natural-language-processing"><span style="font-style: italic;">Introduction to natural language processing</span></a>
by </font><font size="+1">Jacob </font><font size="+1">Eisenstein, </font><font size="+1">2019.</font></li>
<li><font size="+1"><a style="font-style: italic;" href="https://web.stanford.edu/%7Ejurafsky/slp3/">Speech and Language Processing</a> by Dan Jurafsky and James H. Martin, 2023.<br>
</font></li>
</ul>
<li><font size="+1">Robotics books</font></li>
<ul>
<li><font size="+1"><a style="font-style: italic;" href="https://petercorke.com/rvc/home/">Robotics, Vision and Control</a> by Peter Corke, 2017.<br>
</font></li>
</ul>
<li><font size="+1">Other foundations</font></li>
<ul>
<li><font size="+1"><span style="font-style: italic;">Information Theory, Inference, and Learning Algorithms</span> by David J.C. MacKay.</font></li><li><font size="+1"><span style="font-style: italic;">Bayesian Data Analysis</span> by Andrew Gelman.</font></li><li><font size="+1"><span style="font-style: italic;">Nonlinear Programming</span> by Dimitri P. Bertsekas.</font></li><li><font size="+1"><span style="font-style: italic;">Causality</span> by Judea Pearl.</font></li>
</ul>
<li><font size="+1">AI impacts in the world</font></li>
<ul><li><font size="+1"><a style="font-style: italic;" href="https://en.wikipedia.org/wiki/Superintelligence:_Paths,_Dangers,_Strategies">Superintelligence:
Paths, Dangers, Strategies</a> by Nick Bostrom, </font><font size="+1">2014.</font></li><li><font size="+1"><a style="font-style: italic;" href="https://people.eecs.berkeley.edu/%7Erussell/hc.html">Human Compatible: Artificial Intelligence and the Problem of Control</a>, Stuart J. Russell, 2019.</font></li>
</ul>
<li><font size="+1">Courses:</font></li>
<ul>
<li><font size="+1"><a href="https://www.coursera.org/course/ml">ML course</a> by Andrew Ng</font></li>
</ul>
<ul>
<li><font size="+1"><a href="https://www.coursera.org/course/pgm">Probabilistic graphical
models</a> by Daphne Koller</font></li>
</ul>
<ul>
<li><font size="+1"><a href="http://cs229.stanford.edu/">Stanford
ML class</a> <br>
</font></li>
</ul>
<li><font size="+1">Cool videos</font></li>
<ul>
<li><font size="+1"><a href="http://dustintran.com/blog/video-resources-for-machine-learning/">Video
resources</a>.</font></li>
</ul>
<li><font size="+1">Cool blogs</font></li>
<ul>
<li><font size="+1"><a href="http://inverseprobability.com/blog.html">Neil Lawrence's blog</a>.</font></li>
<li><font size="+1"><a href="http://ruder.io/">Sebastian
Ruder's blog</a>.</font></li>
<li><font size="+1"><a href="http://www.inference.vc/">inFERENCe</a></font><font size="+1">.</font></li>
</ul>
<li><font size="+1">Who is who and what they say:</font></li>
<ul>
<li><font size="+1"><a href="http://www.reddit.com/r/MachineLearning/comments/25lnbt/ama_yann_lecun/">Yann
LeCun</a>, <a href="http://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_geoffrey_hinton/">Geoff
Hinton</a>, <a href="http://www.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/">Michael
Jordan</a>, <a href="http://www.reddit.com/r/MachineLearning/comments/2xcyrl/i_am_j%C3%BCrgen_schmidhuber_ama/">Jürgen
Schmidhuber</a>, <a href="http://www.reddit.com/r/MachineLearning/comments/1ysry1/ama_yoshua_bengio/">Yoshua
Bengio</a> and <a href="http://www.reddit.com/r/MachineLearning/comments/32ihpe/ama_andrew_ng_and_adam_coates/">Andew
Ng</a>.</font></li>
<li><font size="+1"><a href="https://www.amazon.com/Architects-Intelligence-truth-people-building-ebook/dp/B07H8L8T2J"><span style="font-style: italic;">Architects of intelligence</span></a> by
Martin Ford 2018.<br>
</font></li>
</ul>
<li><font size="+1">AI/ML research:</font></li>
<ul>
<li><font size="+1"><a href="https://truyentran.github.io/talks/ML2019.pdf">Modern AI/ML
research</a></font></li>
<li><font size="+1">Keeping yourself informed by following people on Twitter<span style="text-decoration: underline;"></span><a href="http://www.arxiv-sanity.com/"></a></font></li>
</ul>
<li><font size="+1">AI/ML topics<br>
</font></li>
<ul><li><font size="+1">Reinforcement learning.</font></li>
<li><font size="+1">NLP</font></li>
<li><font size="+1">Computer vision</font></li>
<li><font size="+1">Machine reasoning</font></li>
<li><font size="+1">Safety AI and value alignment</font></li>
<li><font size="+1">Consciousness</font></li>
<li><font size="+1">Quantum ML</font></li>
<li><font size="+1">Cognitive architecture</font></li>
</ul>
</ul>
<font size="+1"><span style="font-weight: bold;">Advices (General)</span></font><br>
<ul>
</ul>
<font size="+1"><span style="font-weight: bold;"></span></font>
<ul>
<li><font size="+1"><a href="http://www.paulgraham.com/hamming.html">You and your research</a>
by Richard Hamming, aka, <span style="font-style: italic;">how to win
Nobel Prize</span>.</font><br>
<font size="+1"><a href="http://www.amazon.com/Letters-Young-Scientist-Edward-Wilson/dp/0871403773"><span style="font-style: italic;"></span></a></font></li>
<li><font size="+1"><a href="http://www.amazon.com/Letters-Young-Scientist-Edward-Wilson/dp/0871403773"><span style="font-style: italic;">Letters to a young scientist</span></a> by
Edward O. Wilson, aka, <span style="font-style: italic;">you don't
need to be very smart</span>.<br>
</font></li>
<li><font size="+1"><a href="https://www.amazon.com/Advice-Young-Investigator-MIT-Press/dp/0262681501"><span style="font-style: italic;">Advice for a young investigator</span></a>
by Santiago Ramón y Cajal, aka, <span style="font-style: italic;">how
to do top-notch research when you're poor</span>.<br>
</font></li>
<li><font size="+1">How to pick right thesis topics:</font></li>
<ul>
<li><font size="+1"><a href="http://simplystatistics.org/2014/04/22/picking-a-biostatistics-thesis-topic-for-real-world-impact-and-transferable-skills/">How
to pick a biostatistics thesis topic</a></font></li>
<li><font size="+1"><a href="http://simplystatistics.org/2013/05/29/what-statistics-should-do-about-big-data-problem-forward-not-solution-backward/">Pick
problem first</a>.</font></li>
</ul>
<li><font size="+1"><a href="http://thesiswhisperer.com/buy-the-thesis-whisperer-ebooks/"><span style="font-style: italic;">How to tame your PhD</span></a> by Dr
Inger Mewburn, also author of <a href="http://thesiswhisperer.com/">Thesis
Whisperer blog</a>.</font></li>
<li><font size="+1"><a href="http://people.ischool.berkeley.edu/%7Ehal/Papers/how.pdf">How to
Build an Economic Model in Your Spare Time</a></font></li>
<li><font size="+1"><a href="http://www.unifr.ch/wipol/assets/files/PhD%20Course/JEP92_Hamermesh.pdf">The
Young Economist's Guide to Professional Etiquette</a></font></li>
</ul>
<br>
<font size="+1"><span style="font-weight: bold;">Innovation,
creativity and futurists</span><br>
</font>
<ul>
<li><font size="+1"><a href="https://en.wikipedia.org/wiki/Edward_de_Bono">de Bono</a>’s <span style="font-style: italic;"><a href="https://en.wikipedia.org/wiki/Six_Thinking_Hats">six thinking
hats</a> & <a href="https://en.wikipedia.org/wiki/Lateral_thinking">lateral
thinking</a>.</span></font></li>
<li><font size="+1">TRIZ - Theory of Inventive Problem Solving:
<a href="https://en.wikipedia.org/wiki/Level_of_invention">5
levels of inventiveness</a> | </font><font size="+1"><a href="http://www.southampton.ac.uk/%7Ejps7/Lecture%20notes/TRIZ%2040%20Principles.pdf">40
inventive principles</a> </font></li>
<li><font size="+1">Top 10 breakthroughs by MIT Tech Review</font><font size="+1"><a href="http://www.technologyreview.com/lists/breakthrough-technologies/2013/"></a></font></li>
<li><font size="+1">Futurists<br>
</font></li>
<ul>
<li><font size="+1"><a style="font-style: italic;" href="http://www.singularity.com/">The singularity is near</a> by Ray
Kurzweil, 2005</font></li>
</ul>
</ul>
<font size="+1"><span style="font-weight: bold;"></span><span style="font-weight: bold;">Research methods, research skills and
dealing with PhD process and supervisors</span><br>
</font>
<ul>
<li><font size="+1">Philosophy of sciences: Popper's <a href="https://en.wikipedia.org/wiki/Falsifiability">falsifiability</a>
| Kuhn's <a href="https://en.wikipedia.org/?title=The_Structure_of_Scientific_Revolutions">paradigm</a></font></li>
<li><font size="+1"><a href="http://nlpers.blogspot.com.au/2014/06/role-models.html">Role
model</a>.</font></li>
<li><font size="+1">Publishing papers</font></li>
<ul>
<li><font size="+1"><a href="http://www3.ntu.edu.sg/home/assourav/crank.htm">CS conference
ranking</a></font></li>
<li><font size="+1"><a href="http://103.1.187.206/core">Australian
CORE2014 conference ranking</a></font></li>
<li><font size="+1"><a href="https://scholar.google.com.au/citations?hl=en&view_op=search_venues&vq=machine+learning">Google
Scholar metric</a></font></li>
<li><font size="+1">A fresh perspective: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1681694/">Writing
medical journal papers</a>, also <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2218563/pdf/canfamphys00188-0083.pdf">here</a>.</font></li>
<li><font size="+1"><a href="http://www.bramvanginneken.org/2007/12/my-life-as-associate-editor-of-tmi.html">The
reviewing process</a>.</font></li>
</ul>
<li><font size="+1">Writing</font></li>
<ul>
<li><a href="http://www.deeplearningindaba.com/uploads/1/0/2/6/102657286/research-paper-writing.pdf"><big>Great
advices from top people in ML</big></a>.</li>
<li><a href="http://approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/"><font size="+1">Writing for machine learning</font></a></li>
<li><font size="+1">LaTex and LyX</font></li>
<li><font size="+1"><a style="font-style: italic;" href="http://en.wikipedia.org/wiki/The_Elements_of_Style">Elements of
Style</a> by William Strunk<span style="font-style: italic;"></span></font></li>
<li><font size="+1"><span style="font-style: italic;">On
writing</span> by Stephen King.</font></li>
<li><font size="+1">Coursera course: <a style="font-style: italic;" href="https://www.coursera.org/course/sciwrite">Writing in the Sciences</a>
by Dr. Kristin Sainani of Stanford</font></li>
<li><font size="+1"><a href="http://blog.geomblog.org/2013/11/the-many-stages-of-writing-paper-and.html">Stages
of writing a paper</a>.</font></li>
<li><font size="+1"><a href="https://nips.cc/Conferences/2013/PaperInformation/EvaluationCriteria">NIPS
2013 author guide</a>.</font></li>
<li><font size="+1"><a href="http://homes.cs.washington.edu/%7Emernst/advice/progress-report.html">Progress
report</a>.</font></li>
<li><font size="+1"><a href="http://homes.cs.washington.edu/%7Emernst/advice/write-technical-paper.html">Technical
paper</a>.</font></li>
</ul>
</ul>
<font size="+1"><span style="font-weight: bold;"></span></font><font size="+1"><span style="font-weight: bold;">Data science</span><br>
<span style="font-weight: bold;"></span></font>
<ul>
<li><font size="+1"><a href="http://kaggle.com/">Kaggle.com</a>
-- the competition platform.</font></li>
<li><font size="+1"><a href="http://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/">A
generic framework</a>.</font></li>
<li><font size="+1"><a href="http://nirvacana.com/thoughts/becoming-a-data-scientist/">Road
map to data scientist</a></font></li>
<li><font size="+1">Book: <a href="http://infolab.stanford.edu/%7Eullman/mmds.html">Mining of
massive datasets</a> by Jure Leskovec, Anand Rajaraman, Jeff Ullman</font></li>
<li><font size="+1">YouTube: <a href="https://www.youtube.com/watch?v=yvDCzhbjYWs">unreasonable
effectiveness of data</a> by Peter Norvig, Google Director of Research.</font></li>
<li><font size="+1"><a href="http://informaticsprofessor.blogspot.com.au/2013/10/gimme-some-analytics-we-already-have-it.html">Skill
set for data analytics</a>.</font></li>
<li><font size="+1"><a href="http://blog.geomblog.org/2014/08/interdisciplinary-research-and.html">Skill
set for even more serious data research</a>.</font></li>
<li><font size="+1">Coursera's <a href="https://www.coursera.org/specialization/jhudatascience/1?utm_medium=catalog">Data
science stream</a> by Johns Hopkins</font></li>
<li><font size="+1"><a href="http://www.network-science.org/">Network
analysis</a></font></li>
<li><font size="+1"><a href="http://multithreaded.stitchfix.com/blog/2015/07/30/gam/">A nice
intro to GAM</a></font></li><li><font size="+1"><a href="https://www.moore.org/programs/science/data-driven-discovery/">Funding
for data-driven discovery</a>.</font></li>
</ul>
<font size="+1"><span style="font-weight: bold;"></span><span style="font-weight: bold;"></span><span style="font-weight: bold;"></span><span style="font-weight: bold;"></span></font><font size="+1"><span style="font-weight: bold;">Startups</span><br>
</font>
<ul>
<li><font size="+1"><a href="http://www.paulgraham.com/articles.html">Paul Graham</a></font></li>
<ul>
<li><font size="+1"><a href="http://www.paulgraham.com/startupideas.html">How to get startup
ideas -- living in the future</a>.</font></li>
</ul>
<li><font size="+1"><a href="http://www.startupschool.org/">Y
Combinator</a></font></li>
<li><font size="+1"><a href="https://i-lab.harvard.edu/venture-incubation/venture-teams/teams?tid_1%255B%255D=110">Hi:
Harvard innovation lab</a></font></li>
<li><a href="https://techcrunch.com/"><font size="+1">TechCrunch
news</font></a></li>
<li><font size="+1"><span style="font-style: italic;">Founders
at work</span> by Jessica Livingston </font></li>
<li><font size="+1"><span style="font-style: italic;">Zero to
One</span> by Peter Thiel</font></li>
<li><font size="+1"><span style="font-style: italic;">Steve Jobs</span>
by Walter Issacson</font></li>
<li><font size="+1"><a href="http://www.howgoogleworks.net/"><span style="font-style: italic;">How Google Works</span></a> by Eric
Schmidt & Jonathan Rosenberg</font></li>
</ul>
<font size="+1"><span style="font-weight: bold;">Jobs, tech
industry & career advic</span>e<br>
</font>
<ul>
<li><font size="+1"><a href="https://terrytao.wordpress.com/career-advice/">Terrence Tao's
career advice (maths focused)</a></font></li>
<li><font size="+1"><a href="http://hunch.net/?p=2772">Academic
job requirements</a>.</font></li>
<li><font size="+1"><a href="http://matt-welsh.blogspot.com.au/2014/01/getting-job-at-google-for-phd-students.html">Getting
Google's jobs</a>, also <a href="http://www.nytimes.com/2014/02/23/opinion/sunday/friedman-how-to-get-a-job-at-google.html?_r=2">the
nature of jobs</a>.</font></li>
<li><font size="+1"><a href="https://culurciello.medium.com/startup-academia-or-big-company-f641881729a5">Choosing the destination: Startup, Big Co, or Academia</a>.<br>
</font></li>
<li><font size="+1">Career paths</font></li>
<ul>
<li><font size="+1"><span style="font-style: italic;">How to
Rise to the Top...and Stay There!</span> by Alexander R. Margulis</font></li>
</ul>
<li><font size="+1">Postdocs</font></li>
<ul>
<li><font size="+1"><a href="https://github.com/BenLangmead/langmead-lab/blob/master/postdoc_questionnaire.md">Career
questionnaire</a> by Ben Langmead</font></li>
</ul>
</ul>
<ul>
</ul>
<big><font size="+2"><span style="font-weight: bold; color: rgb(0, 102, 0);"></span></font> </big>
<ul>
</ul>
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</tr>
</tbody>
</table>
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