Research Scientist Internship

**Introduction**
IBM has launched a number of Discovery Accelerators globally to increase the rate at which we discover solutions to our scientific challenges. This isn’t about working faster, this is about taking advantage of the convergence of AI, cloud, and quantum computing. Within the UK we launched the Hartree National Centre for Digital Innovation (HNCDI) with the UK’s Science and Technology Facilities Council. The goal for this Discovery Accelerator is to solve some of the most ambitious problems in science, business, and society, deploying high-performance computing, AI, quantum computing, and hybrid cloud from IBM Research. The centre is fuelling collaborations across industries and research ecosystems in fields as diverse as life sciences, environmental sustainability, and material science.

We are pleased to advertise an internship in our AI-Enriched Simulation team. The internship will involve the collation, deployment and evaluation of data-driven surrogate models for chemistry. Data-driven surrogates, such as the neural potential ANI (Chem. Sci., 2017,8, 3192-3203) have the potential to revolutionise the way we use computers to understand the world through simulation. Understanding where these models perform well, and where they do not is an important stage in transitioning them from research outcome to impactful asset. We will be seeking to evaluate the performance of data-driven surrogates, and potentially other ML-derived potentials on a range of tasks, including reproducing energy landscapes of complex molecules, and transitioning from molecules to include inter-molecular interactions. We anticipate this work will inform new ways of generating training data for building new surrogates, and potentially inform the choice of particular surrogates for particular tasks.

**Your Role and Responsibilities**
Modelling and Simulation remains a key technology for scientific discovery. In addition to embracing new paradigms for calculation, for example cloud and quantum, IBM are actively pursuing the use of AI to accelerate modelling and simulation workflows. The foundational technology - simulation team have three specific focus areas:

- Simulation tech for science; Using the latest technology and methodologies to develop and enable cutting edge scientific simulations
- AI enriched simulation; Driving differentiation through the use of AI to accelerate time to insight by both accelerating individual simulations through the use of data-driven surrogate modelling, and the intelligent orchestration of simulation campaigns through Bayesian optimization
- Simulation workflow technology - developing differentiated workflow technology to integrate the latest simulations with the latest AI enrichment in a hybrid cloud infrastructure.

Your role as a member of this team will be to:
1. Develop a deep understanding of the various approaches for deep learning (and other ML) surrogates for chemistry

2. Drive project outcomes as part of a team

3. Evaluate different approaches through the design and creation of initial prototypes.

4. Contribute to the communication of the outcome and impact of your research, both internally, and externally, through publications in top-tier scientific journals and conferences, and presentations in relevant forums

**Required Technical and Professional Expertise**
We are looking for research scientists with at least a graduate qualification in Applied Mathematics, Computer Science, Physics, Material Science, Artificial Intelligence, Machine Learning, or related topics.

Our requirements for you to demonstrate are;
- Expertise in deploying data-driven surrogate methodologies for scientific simulation workflows, preferentially chemistry / materials science but certainly in the physical sciences.
- Strong programming skills ideally including at least one common AI framework
- Your strong academic record, ideally with publications in top peer reviewed scientific journals and/or conferences.
- Your ability to evaluate and showcase research outcomes through your advanced work, particularly through development of protypes.
- Your ability to listen to others, establishing requirements and formulate problems derived from real-world use cases. Then to be able to carry out the necessary research, design, build and validation of successful solutions.
- Your strong communication, listening and collaboration skills
- Excellent command of the English language, both verbally and in writing

**Preferred Technical and Professional Expertise**
As a preference you will also be able to show; A Ph.D. in Applied Mathematics, Computer Science, Physics, Material Science, Artificial Intelligence, Machine Learning, or related topics.
- Some experience working in multi-disciplinary collaborations involving a mixture of academic and industrial partners
- Track record of cross-disciplinary expertise and execution; for example using AI techniques in a simulation environment

**About Business Unit**

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