109 computer-programmer-"https:"-"UCL"-"https:"-"https:"-"https:" Fellowship positions at Harvard University
Sort by
Refine Your Search
-
interpersonal and communication skills. While not a must, a strong background in computational methods and/or statistical methods is a plus. Special Instructions Applicants should submit a formal application and
-
, that means some museum and fieldwork!). Comparative analysis using advanced computational tools and wet lab techniques. Hands-on dissections of invertebrates for anatomical and physiological studies. Leading
-
is research excellence and fit with the lab’s focus. More information on the lab’s research is available here . We especially encourage candidates with proven experience in applying computational and
-
What You’ll Need: PhD in computer science, artificial intelligence, machine learning, computational biology, biomedical engineering, or a closely related quantitative field. Strong foundation in modern
-
history that has taken place largely in Pakistan, and plan to return to Pakistan upon completion of the fellowship. Basic Qualifications Applicants must hold a PhD. Additional Qualifications Special
-
for undergraduate and graduate students. Qualifications: 1. A Ph.D. in Neuroscience, Molecular Biology, Genetics, Computer Science, or other relevant scientific discipline is required. 2. Basic understanding
-
Mathematics / Computer Science Position Description Professors Le Xie and Na Li in the John A. Paulson School of Engineering and Applied Sciences (SEAS) at Harvard University seek a motivated postdoctoral
-
Requirements Strong computing and strong background/expertise in clustered data, survival data, causal inference or measurement error are desired. Strong written communications Additional Information: Per
-
place across the departments of Physics, Chemistry and Chemical Biology, Mathematics and the School of Engineering and Applied Sciences. Active research areas include quantum information and computer
-
sophistication, including strong statistical skills and comfort with large-scale or complex data. Experience with computational text analysis, such as NLP methods, historical text processing, topic modeling