Openings

We are looking for a highly motivated PhD student for our activities in brain-inspired computing.

Project description

We aim to advance the understanding and capabilities of oscillating neural networks (ONN) with the goal of developing technologies for future neuromorphic computing. Activities comprise designing, simulating and fabricating and analyzing nanoscale devices and machine-learning circuits. In particular, electrical and thermal characterization experiments should be performed. Furthermore, we will implement and simulate machine learning task such as pattern and pattern recognition using these ONN. The offered position will be associated with the EU HORIZON 2020 project NeurONN.

The successful candidate will have access to a state-of-the-art exploratory research facility for the fabrication and characterization of nanoelectronic devices and circuits including a fully equipped clean room as well as access to a powerful compute infrastructure.

Essential requirements

Applicants are expected to hold a Master’s degree in electrical engineering, computer science, physics or a related field. The ideal candidate is very talented, creative, highly motivated and has excellent communications skills and is open to working in an international, multidisciplinary team.

How to apply

Your application should be submitted using the apply button below and include the following:

• A full CV
• Relevant university transcripts
• Two references, or contact details of potential referees

Please be advised that the application material may be shared with relevant staff at IBM as part of the application process.

This position is available starting immediately for a duration of three years in a collaborative and creative group in a lively research environment.

Questions? For more information on technical questions please contact Dr. Siegfried Karg (sfk@zurich.ibm.com).

Diversity

IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent, flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.

We are looking for a Post-Doc candidate to conduct research in the European H2020 NEURONN project in collaboration with several academic and industrial partners.

Activities

The objective of this work is on device modeling and simulation of VO2 and MoS2 memristors. The goal is to explore device simulation approaches starting from ab initio (first-principle) methods, going through TCAD device modeling and up to SPICE circuit simulations and modeling. The main motivation for using such a multi-scaled approach is that the complexity simulation is reduced while preserving device physics’ accuracy. The first objective is to develop detailed modeling with an accurate description of the electronic structure, surface defects and atomistic growth requires computations based on the quantum-mechanical (QM) methods, such as Density Functional Theory (DFT) and Molecular Dynamics (MD). The second objective is to develop an accurate and realistic description of device geometry and characteristics, such as doping, thermal and electron transport, demands continuous modeling approximations applied to systems with millions of atoms and realistic device architectures, e.g. Drift-Diffusion (DD) and Non-Equilibrium Green’s Function (NEGF) formalism. Therefore, the goal is to develop a multi-scale simulation based on different modeling approaches, so it represents the device physics (of VO2 and MoS2) as accurately as possible.

Skills

Excellent and self-motivated candidates with a PhD degree in Electrical Engineering, Computer Engineering, Applied Physics, Engineering Physics, Solid-state Physics, Computational Physics or Materials Science with very good marks.
Experience in first-principles atomistic simulations (time-dependent density-functional theory calculations, molecular dynamics, etc.)
Good scripting and programming skills
Excellent written and oral communication skills in English
Readiness to work in an international team and closely collaborate with experimentalists

We are looking for a PhD student that will investigate neuro-inspired computing architecture where information is encoded in the phase of coupled oscillating neurons or oscillatory neural networks (ONN).

Work Context

The objective of this work is to investigate the full potential of ONN circuits and architectures. In particular, understanding of the interplay between MIT devices and coupling strengths via 2D memristors on phase synchronization, phase difference and scalability to build large-scale ONN architectures. We will also investigate MIT device and 2D memristor process variations and impact on ONN architecture performance and power efficiency. Ultimately, we will investigate and assess the application of associate learning problems such as pattern recognition on ONN architecture.