Genesis Therapeutics

Education Requirements:

• B.S./M.S. in Biochemistry, Biophysics, or a related field

Experience Requirements:

• minimum 1 year of academic or industry lab experience.

Responsibilities:

• Design and execute routine biophysical assays from beginning to end using methods such as SPR, DSF, and other biophysical techniques to characterize small molecule-protein interactions.

• Routinely and independently analyze & QC experimental data.

• Program, validate, and operate liquid-handling automation (Biomek Liquid handler, Multidrop Combi, Integra Viaflo & Voyager) for 384-well plate-based assays and use instrumentation such as plate readers for reproducible, publication-quality data generation.

• Communicate results efficiently & precisely in written format by maintaining electronic lab notebook.

• Presents work to supervisor, lab and/or project meeting.

Benefits:

• Competitive Pay

• Health Care Plans (including Medical, Dental, and Vision, fully covered for the employees)

• Stock Option Eligibility

• 401(k) Plan

• Open PTO Policy Paid Company Holidays Free lunch and snacks at our offices

Education Requirements:

• A BSc, MSc, or PhD degree in immunology, molecular and cell biology, biochemistry or a related field

Experience Requirements:

• 8+, 5+ or 2+ years of relevant industry experience, respectively.

Responsibilities:

• Autonomously plan, design, and execute biochemical and cell-based experiments to support our I&I pipeline.

• Maintain a hands-on approach while being an open-minded, flexible team player.

• Analyze, interpret and integrate complex datasets across multiple assay types.

• Document data meticulously using ELN and state-of-the-art analyses.

• Stay at the forefront of latest research through literature review and critical evaluation of available data sources.

Education Requirements:

• Currently enrolled in a PhD program in Computer Science, Machine Learning, or related fields.

Responsibilities:

• Lead a research project, which will be broadly focused on a problem related to generative or predictive modeling of molecular systems.

• Navigate the latest deep learning literature, extracting insights from generative modeling in adjacent domains (language, images, 3D graphics, etc.), develop novel models and training techniques for molecular data, and communicate your findings to the team.

• Carefully design and run experiments at scale to validate most promising approaches and hypotheses.