Deep Learning Algorithms for Budget Constrained Applications in the IoT Domain
The IBM Research Laboratory in Zurich is leading the design of innovative automated machine learning and deep learning platforms, targeting enterprises that aim to replace or accelerate traditional human-supervised procedures, with automated services based on AI models. Toward this goal, we are looking to strengthen our team with a highly motivated and skilled scientist that will contribute to the design of such solutions as part of a prestigious
European ITN Project - APROPOS.
Given their size and computational complexity, deep learning approaches cannot be easily employed at scale in many real applications. Common issues are indeed execution time, memory footprint, and complexity of long-term model lifecycle maintenance (e.g.,re-training models for new incoming data or adapting models to fit new user constraints). Additionally, they generally do not easily fit edge devices, due to their limited power and memory budget. However, with efficient algorithm design we can adapt these complex methods to better scale on limited computing resources. The candidate will develop and evaluate novel algorithms for fast execution of inference models on traditional bare metal machines, on cloud nodes, as well as on edge devices. The research space will cover compression/quantization algorithms, neural network model optimization under user constraints, as well as any other possible close topic that might help us to not only improve memory footprints but also to minimize application run time without sacrificing accuracy. The candidate will also develop efficient edge2cloud execution flows, in a hybrid framework that will enable to process part of the data on edge and the rest on the IBM Cloud. Different data modalities, such as images, text, tabular data, and various sorts of sensor data, provide a challenging setting of applying novel approaches. Henceforth, we adopt our research to deliver tomorrow’s AI solutions that follow the needs of our clients. Indeed, the results of the work will be applied to real use cases, in connection with IBM Clients and SME projects in the IoT domain.
- Develop, standardize, and implement data science and machine learning solutions at scale for edge devices, as well as hybrid cloud systems
- Create new algorithms for near real-time optimization of models for computer vision, NLP, tabular data, and/or time series, accounting for user/applications constraints on execution time, memory, and power budget, among others
- Leverage techniques such as quantization, low-precision algorithms, compression, as well as others, to fit the user/application constraints
- Use the developed models and algorithms inside AI workflows for model lifecycle management, such as (for example) bias mitigation, drift detection, explainability, and constrained optimization
- Design, develop, and implement proof-of-concepts and prototypes to be ported and included on the IBM Public Hybrid Cloud offering
Minimum qualifications (mandatory)
- Outstanding university track record, with background in Computing, Machine Learning, Mathematics, Statistics, or equivalent fields.
- 3+ years of proved programming experience in C/C++ and/or Python
- Proficient in UNIX/Linux
- Ability to speak and write in English fluently
- Self-motivated with passion for technology and innovation
The candidate will work full time at the IBM Research Europe Laboratory in Zurich, having the opportunity to work in a unique corporate environment, acquire experience in several areas, publish in top international conferences, learn how to patent innovative ideas, as well as deal with clients on real business cases. The candidate will also spend up to 6 months at Universitat Politecnica de Valencia (UPV), where he/she will also get his PhD degree in Computer Science.
Conditions to apply
The researcher must not have resided or carried out their main activity (work, studies, etc.) in the country of the host organization (Switzerland) for more than 12 months in the 3 years immediately prior to the start date of the PhD.
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.
How to apply
If you are interested in this position, please submit your most recent curriculum vitae including a transcript of grades on the following page.