Physics-informed Machine Learning Scientist

5 days ago

Bengaluru District Karnataka, India Grafton Biosciences Inc Full time

**Company Description**: Grafton Biosciences is a biotech startup focused on solving disease through groundbreaking innovations in early detection, diagnostics, and therapeutics. We are combining cutting-edge molecular and synthetic biology, machine learning, device engineering, and manufacturing to fundamentally extend healthy human lifespans. We’re looking for passionate team members who want to shape the future.

**Key Responsibilities**:

- Lead the development, implementation, training, and rigorous validation of high-accuracy **Machine Learning Potentials (MLPs)** for biomolecular systems, potentially collaborating closely with the simulation lead.
- Design and implement advanced **surrogate models** (e.g., using Gaussian Processes, Bayesian Neural Networks, or other relevant techniques) based on simulation data for rapid and accurate property evaluation within the design optimization loop.
- Design and implement **Active Learning** strategies, leveraging UQ to guide the efficient generation of QM data or selection of informative simulations.
- Ensure physical consistency (e.g., via physics-informed architectures or loss functions) is incorporated appropriately into ML models across the platform.
- Collaborate closely with simulation and generative AI leads to integrate physics-ML models seamlessly into the overall workflow.

**Required Qualifications**:

- Ph.D. in a relevant field requiring strong quantitative and physics/chemistry background (e.g., Comp Physics/Chemistry/Materials Sci, Applied Math/Stats, CS with physics focus).
- Deep expertise in relevant **advanced ML and statistical modeling techniques** (e.g., Gaussian Processes, Bayesian methods, Neural Networks applied to physical systems, UQ techniques).
- Strong foundation in **Statistical Mechanics, Thermodynamics**, and relevant physics/chemistry principles.
- Strong programming skills in Python, including relevant ML/stats libraries (e.g., PyTorch/TensorFlow/JAX, GPy/GPflow, PyMC/Stan, Scikit-learn) and scientific computing libraries.
- Excellent analytical and quantitative reasoning skills.
- Demonstrated ability to bridge theoretical concepts with practical implementation.
- Excellent communication and collaboration skills.

**Preferred Qualifications**:

- Experience specifically with Gaussian Processes for complex modeling or UQ.
- Experience with active learning strategies in a scientific context.
- Familiarity with biomolecular simulation concepts or data.
- Experience with high-performance computing (HPC).

**Who You Are**:

- Possess a unique blend of strong theoretical understanding and hands-on implementation skills.
- Operate with high agency, proactively identifying challenges, defining solutions, and driving progress with no supervision in a fast-paced, ambiguous environment.
- Highly analytical, rigorous, and detail-oriented.
- An innovative thinker capable of developing novel methodologies.
- An excellent collaborator who can work effectively at the interface of ML, physics, and biology/chemistry.
- Excited by the fast-paced environment of an early-stage startup.

Pay: ₹6,000,000.00 - ₹7,500,000.00 per year

Supplemental Pay:

- Performance bonus

Application Question(s):

- Why are you a fit for this role?
- How soon can you begin?

Work Location: In person

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