Leverage your professional network, and get hired. g. This course focuses on modern machine learning methodologies for computational problems in molecular biology and genetics, including probabilistic modeling, inference and learning algorithms, pattern recognition, data fusion, time series analysis, etc. We work at the intersection of machine learning, statistical modeling, and experimental biology, with a focus on robust and reproducible analytics for modern single-cell modalities. Jan 10, 2026 · Preferred Education: Ph. Nature Methods, 21 (8):1454-1461, 2024. The position is based in Sunnyvale, CA, with consideration for candidates located remotely or in the Boston, MA area. A supervised machine learning model aims to learn a function f(x) y from a list of training pairs (x1,y1), (x2,y2), for which data are recorded (Fig 1B). Through a set of systematically selected case studies, we highlight how machine learning models have been used in a range of applications Oct 1, 2025 · Machine Learning is fundamentally transforming computational biology, enabling researchers to tackle biological questions that were previously intractable due to data complexity and scale. What is Computational Biology Software? Computational biology software refers to an array of specialized tools developed to analyze, simulate, and interpret complex biological data. Strong understanding of protein structure bioinformatics and/or protein structure prediction and protein structure datasets. 20 hours ago · Developing models that offer insight into the decision-making processes of machine learning algorithms will be crucial for refining predictions and gaining a deeper understanding of B-cell biology. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets. Aug 9, 2024 · This Perspective discusses the methodologies, application and evaluation of interpretable machine learning (IML) approaches in computational biology, with particular focus on common pitfalls when This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. One typical application in biology is to predict the viability of a cancer cell line when exposed to a chosen drug (Menden et al, 2013; Eduati et al, 2015). The literature systematically reviews papers in recent five years and introduces a novel classification for Job type Postdoc Field Bioinformatics, Genetics, Computational Biology, Programming Languages, Machine Learning and 3 more Apply now Save job Share this job Designing and optimizing proteins takes a lot of expert knowledge and manual effort, through the use of custom computational and biological tools. Required Experience: Genentech, Inc. Applying interpretable machine learning in computational biology - pitfalls, recommendations and opportunities for new developments. We cover both foundational topics in computational biology, and current research frontiers. We are a cross-disciplinary team of experts in machine learning, structural biology, computational chemistry, and bioinformatics, supported by strong engineering infrastructure and access to large-scale compute resources. New Computational Biology Internship jobs added daily. Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, Computational Biology, or a related quantitative field with 8 years of related industry experience Demonstrated deep technical expertise in developing custom AI/ML models for medical imaging The PNC Ph. This job in Pharmaceuticals & Biotech is in South San Expanding the immunotherapy toolkit with protein design AI Speaker: Possu Huang, Ph. 1 day ago · Master’s degree in Machine Learning, Computational Biology, Statistics, Computer Science, Mathematics, or a related technical field, or the equivalent combination of education and related 2 days ago · You will apply expertise in bioinformatics, genomics, machine learning, and computational biology to integrate and analyze pre-clinical and clinical, internal and public multi-omics datasets, to accelerate Takeda’s Neuroscience pipeline and bring transformative medicines to patients. We begin by introducing fundamental concepts, traditional methods, and benchmark datasets, then examine the various roles ML plays in improving CFD. Or Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, Computational Biology, or a related quantitative field and 8 years of related industry experience Preferred Qualifications: Demonstrated deep technical expertise in developing custom AI/ML models for medical imaging. , systems biology, genetics, molecular biology, physics) in collaborative settings to unravel complex biological questions and communicate domain knowledge to non-computational stakeholders & colleagues. Machine Learning in Computational Biology The 20th Machine Learning in Computational Biology (MLCB) meeting will be a two-day hybrid conference, September 10-11, 9am-5pm ET, with the in-person component at the New York Genome Center, NYC.

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