Career Opportunities for Physicists
Post-doctoral fellow in machine-learning - Dana-Farber Cancer Institute
The Carter lab is seeking a talented and highly motivated post-doctoral fellow in machine-learning to analyze our unique multi-omic characterization of human cancer-tissue samples in order to elucidate basic mechanisms of cancer initiation, progression, and metastasis. The Carter lab has pioneered the application of statistical methods to extract rich data from genomic sequencing of cancer tissue-samples and infer phylogenetic relationships between cancer subpopulations. We collaborate closely with clinical oncologists and genomic technologists across Harvard, MIT, DFCI, MGH, and the Broad Institute in order to build datasets
enabling discovery of key genetic alterations driving adverse cancer outcomes.
The fellow will be expected to lead the development and implementation of novel statistical machine learning algorithms and produce usable analysis pipelines supporting our mission. The fellow will join a strong and diverse team of clinical, experimental, and quantitative researchers across our collaborating labs. Successful candidates are expected to excel at critical thinking, be quick learners for new analytical approaches, and capable of applying or developing novel computational methods for solving complex problems. The ideal candidate has both a theoretical and practical understanding of either Bayesian statistics or deep-learning techniques and has a proven track-record in areas such as statistics, mathematical modeling,
complex networks data analysis, or statistical physics.
This position is suited to a person who is excited by the prospect of applying their quantitative skills to computational biology with a strong quantitative somatic genetics focus.
Roles and Responsibilities:
• Work with PI to develop machine learning methods for inference and classification of our datasets
• Writing usable, maintainable, and documented machine learning code
• Support analysis by biologist colleagues without formal computational training
• Publish new methods and results in academic journals and conferences
• Present findings to internal and external multidisciplinary audiences in a clear and cohesive manner
• Follow relevant scientific literature to ensure use of optimal methods and understand emerging
practices across the field, including testing and evaluating newly developed software tools and methods as they become available
• Acquire and process external datasets relevant to our research
• Regularly attend and present at lab and project team meetings to ensure continuous communication around methods and tools developed
Skills and Abilities:
• A PhD in computer science, engineering, mathematics, physics, or other quantitative fields with a strong computational emphasis
• Experience in cancer biology, genetics, or genomics is helpful, but not required provided the applicant has a strong desire to immerse themselves in these fields
• Expertise in one of Python, R, Julia, or MATLAB
• Strong demonstrated skill in statistical algorithm development / machine learning / deep learning
• Experience with probabilistic programming frameworks such as Python pymc3 is desirable
• Experience with deep learning using TensorFlow, Theano, or Python/keras is desirable
• Experience working with high performance compute clusters and cloud compute solutions a plus
• Independent, highly motivated, highly collaborative and works well with others
• Excellent communication, organization, and time management skills
[posted June 13, 2018]
Professor of Practice in Physics – Tulane University
The Department of Physics and Engineering Physics at Tulane University invites applications for a Professor of Practice (PoP) position in Physics to begin in August 2018 or January 2019. The PoP position is a non-tenured full time academic year (nine month) teaching position (3,3 load) with a renewable 3-year appointment, and it carries a competitive salary and benefits commensurate with the candidate’s qualifications. Faculty promoted to Senior PoP serve renewable 5-year terms.
A doctorate in physics or a closely related field is required. Candidates must be outstanding at teaching and fit well into the goals and activities of our department, which will include teaching a variety of physics courses at the introductory and advanced levels. Applications from candidates who are also interested in teaching astronomy/astrophysics courses are especially welcome.
The Department of Physics and Engineering Physics offers two Ph.D. programs, Physics and Materials Physics and Engineering, and two undergraduate programs, Physics and Engineering Physics. More information about the Department of Physics and Engineering Physics can be found at: tulane.edu/sse/pep. Tulane is a member of the Association of American Universities (AAU).
Application review will begin immediately and continue until the position is filled. Applicants should submit a cover letter, CV, a statement of teaching philosophy, and contact information for three references to apply.interfolio.com/51135 . Questions regarding the position can be addressed to Dr. Lev Kaplan at firstname.lastname@example.org. Women and under-represented minorities are strongly encouraged to apply. Tulane is committed to providing a family friendly workplace.
Tulane is an Equal Opportunity/Affirmative Action/ADA employer.
NOTES: Employer will assist with relocation costs.
[posted June 11, 2018]