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Hello gradcafe folks, I plan to apply for neuroscience PhD program this year. I especially want to study computational cognitive neuroscience. My BS and MS are in Electrical and Computer Engineering. I have been working as a software engineer for 6 years. I have done research in computer vision in undergraduate which I have published a paper and presented in a conference. I have little research experience in neuroscience. The only experience I got so far is doing EEG, ECG, EDA and fMRI data collection as a volunteer intern in a lab in UCSF. I am seeking for some advice in writing my unusual past experience. Here are few possible outlines in my mind: Option 1: go deep about my research in computer vision and how it can blend with computational neuroscience study without mentioning my industrial work in machine learning and my little experience in neuroscience because there is no significant accomplishments from there. Option 2: allocate 2/3 parts to my research in computer vision while briefly use 1/3 parts talking about what I learnt from my industrial work in machine learning and my little research experience in neuroscience. In this way, I can show my diverse experience and how it can prepare me for computational neuroscience study. Curious about what people think about the better way to highlight my own values that can bring into computational cognitive neuroscience. Any other alternatives are welcomed. Thank you for reading this in advance.

Hello, I'm trying to come up with a list of computational neuroscience phd programs. I may also be interested in bioinformatics/neuroscience but ONLY if there is a concentration in neuroimaging-related research, I'm not really into cell biology or genetics and all that jazz. Here's what I have so far, but does anyone know of any other (US-only) programs that are not Ivy League or highly competitive? I'm planning to just send out like <10 applications mostly to not-ivy-league/cali programs. Probably not-reach Programs?: Boston University, Chicago, New York University, Washington State, Wisconsin Milwaukee, Brandeis, Texas Austin, Minnesota, Houston Probably Reach Programs?: Carnegie Mellon, Princeton, Caltech, Southern California, Berkeley, Johns Hopkins, Washington, San Francisco, Emory, Pennsylvania, Columbia, San Diego, Harvard, Los Angeles, Stanford, Yale Correct me if I'm wrong on any of this? I think it'd be great to have a list for comp neuro somewhere on this website... unless it's already here somewhere and I can't find it? Thanks for any help!!

Want to further my studies in computation neuroscience or neuro engineering with a focus on Data analysis/Brain Computer Interface. My academics so far can be summarised on data processing, statistics and software development. I m confused regarding which colleges in US provide this course with emphasis on computer science and not on biology. Till now I have charted out some colleges in Germany and University of Waterloo in Canada. Though I m not able to find connection between the school of computer science with masters in computational neuroscience. Can someone suggest some colleges on this aspect. My GRE score is : Q : 162 , V : 161 .

I recently graduated with a physics degree and plan on applying to PhD's in computational neuroscience. I've only taken four semesters of mathematics (Calc III-IV, Diff Eq I-II) in college so I'm worried that I'm not prepared mathematically. Would taking the GRE Math Subject test improve my chances of admission at Computational Neuroscience PhD programs?

I recently graduated with a physics degree and plan on applying to PhD's in computational neuroscience. I've only taken four semesters of mathematics (Calc III-IV, Diff Eq I-II) in college so I'm worried that I'm not prepared mathematically. Would taking the GRE Math Subject test help in improving my chances of admission at Computational Neuroscience PhD programs?

I am interested in continuing my education in math and I know that I'd eventually like to work on brain-computer interface (theory and application) like mind uploading but was curious if there is a discipline that merges computational neuroscience, biostatistics, AI, and cybersecurity: providing a rigourous curriculum that can be used to pursue these fields. Any input would be greatly appreciated! This is ultimately to maximize my chances of being employed, having a successful career long term. If the opportunity exists, I would equally like to learn more about AI (neural net) and cybersecurity, and I currently enjoy the statistical, predictive modeling (machine learning) work that I do in genetics (similar to data science). I have thoroughly looked through gradcafe, stackexchange, quora, reddit and amassed math topics important in each field. I have highlighted common topics and would like to get you guys' input on the accuracy of this list. MATH TOPICS FOR EACH FIELD cybersecurity - applied number theory (abstract algebra), combinatorics (graph theory), algebraic geometry, information theory, asymptotic analysis, finite fields computational neuroscience - information theory, systems theory (nonlinear dynamics, dynamical systems), evolutionary algorithms (Monte Carlo), state space analysis, signal processing, probability theory AI/ML - neural networks, genetic algorithms, information geometry (Riemannian geometry, information theory, Fisher information), algebraic geometry, manifold geometry, learning theory (Fourier analysis), probability theory, game theory (topology, measure theory), graph theory, Model Free Methods RECOMMENDATIONS Some have recommended biostatistics programs because the curriculum offers a fair amount of 'theoretical' math work. Others, however, have said that biostatistics is a bad choice - sticking to CS or EE would be better. There is always the option to go into pure math but I am concerned about employability of a pure math PhD compared to an applied math PhD. I have played with the idea of work towards becoming a fellow of actuarial science simultaneously instead to gain statistical training - although this would be more oriented towards business, not science There is also the fact that I have a BS in biochemistry. I have done post-bacc work for CS fundamentals, calculus series, diff. eq., linear algebra, statistics, combinatorics, but there is a legitimate chance that I may not have sufficient background for fields (like statistics or applied math) other than biostatistics. I have looked heavily into degrees for applied/computational mathematics, scientific computing (UPENN, Rice, JHU, MIT, Stanford, Maryland) but it seems that these fields are more broadly focused on application reseach for physics, chemistry, biology (like engineering). I've also looked into mathematical biology (aka biomathematics) but it seems not a lot of schools have such a department - it's commonly housed under computational/systems biology. Thank you very much for your time and help!

Hey all. Hoping that I found the right place to post this. I was just looking for some smart people to get a few pieces of advice from, for planning out graduate studies. I finish my undergraduate at the University of Waterloo in a year's time. I'm currently planning to work my way to a PhD, but am unsure of where and what path I should take. My background is in Computer Science and Combinatorics & Optimization. I find myself very interested in the field of machine learning, but feel it's important to also learn psychology and neuroscience. This is because of the many real life problems where the machine learning model involves a component of human interaction. I'll try to be specific: I have found the field of machine learning to be lacking in many of the problem spaces that are prevalent today, namely those where the underlying function that we are trying to learn is a product of the human mind (or even many human minds together). Here, it's important to take into account many behavioral factors in modelling the hypothesis space, and to keep it robust enough to allow for varieties of learning biases (there's no free lunch, etc, etc). Sadly, very little robustness is seen in most techniques, and the human component is generally viewed as more of a black box with algebraic or Bayesian learning biases. I feel it's important to consider techniques that allow for more application specific modelling of the human component. I want to pursue a program that can allow me to explore some of these concepts in greater depth. I am unsure of what programs offer such a perspective. This is where computational neuroscience may come into play. But, I am not entirely sure, since I am only familiar with the machine learning side of things. Ideally, I would be researching new approaches of modelling the human mind and applying those models towards specific real world problems, such as predicting user relevancy and preferences. These are problems that I find all around us, from Facebook, to News websites, to advertising, to our next-level media platforms (e.g. Netflix). These are problems that I know companies are asking, and machine learning literature has begun to answer (e.g. collaborative filtering). Somewhere down the road, I wish to be doing research in these areas, as well as teaching Computer Science and Machine Learning as a professor. I'd like to know what school and program might be the best to get me there. Hopefully that is enough rambling of where my mind is at. I'll just mention some of my credentials, to brag, and to let you know of my capabilities. My undergraduate is a 5 year bachelors co-op, double majoring in Computer Science and Combinatorics & Optimization. My cumulative average is 92% and my 3rd-4th year average is 95%. I have performed at an international level in Computer Science competitions, and have worked for 8 months for a startup in San Francisco as the lead machine learning developer. I should also be able to obtain a decent recommendation from at least one professor at Waterloo. However, I have no experience in Biology. I do have 3rd year experience in Psychology. Sorry if that post was a little long. Just eager and looking forward to any thoughts. Thanks.