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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 7 hours ago
evaluation. Demonstrated proficiency in programming (Python required). Ability to work independently while contributing to collaborative, interdisciplinary research efforts. Knowledge, Skills & Abilities
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, energy poverty, and energy security Prior experience in qualitative, quantitative, or mixed-methods research. Technical competence in data analytics, programming, or modeling using tools such as Python, R
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cancer biology, molecular/cellular biology, genomics, or immunology with expertise in gene editing technology and NGS; flow cytometry and cell sorting; and bioinformatic and data integration, R/Python
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) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence modeling architectures and
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field. - Evidence of strong prior experience analyzing large datasets using advanced econometric and statistical methods (using R, Stata, and/or Python). - Strong interest and experience working
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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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data analysis skills, including experience with R, Python, MATLAB • Excellent written and verbal communication skills • Strong organizational and interpersonal skills and ability to work collaboratively
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candidates with a proven track record in developing open-source machine learning, deep learning, or cheminformatics tools (Preferred written in Python). Job Duties Plans, directs and conducts research
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of education materials). Required Qualifications: PhD in Biostatistics, Computer Science, Bioinformatics, Statistics, Mathematics or related field. Preferred: Proficient in R/Python. Prior experience in genetic
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acquisition, instrument control, and data analysis in Python or similar languages Knowledge of quantum optics measurements such as photon correlation functions or photon-number-resolved detection Familiarity