PhD Position in Energy-Efficient Machine Learning for Wearable and Augmented Reality Systems 100%, Zurich, fixed-term
ETH Zürich
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PhD Position in Energy-Efficient Machine Learning for Wearable and Augmented Reality Systems
100%, Zurich, fixed-term
print Drucken
We have an open PhD position at the intersection of machine learning, embedded intelligence and human–computer
interaction. The project will explore how learning systems can become more adaptive, efficient and context-aware,
creating the foundations for the next generation of wearable and augmented reality platforms.
The research focuses on developing novel ML methods that learn from limited resources, adapt locally to users and
environments, and remain computationally efficient. These models will be explored and benchmarked in software, with
selected approaches translated into embedded prototypes to demonstrate real-world feasibility. The overarching goal is
to bridge high-level algorithmic innovation with energy-aware hardware deployment, enabling intelligent sensor systems
that act as autonomous micro-agents for perception and communication.
Candidates ideally have a background in computer science , electrical engineering, or related fields, and a strong
interest in machine learning, optimization, or intelligent systems. Prior experience with embedded platforms, signal
processing, or model compression is a plus. We value creativity, curiosity, and the ability to connect ideas across
disciplines.
The PhD will be co-supervised by Dr Christoph Leitner and Prof Luca Benini in the Digital Circuits and Systems Group
at the Integrated Systems Laboratory (D-ITET, ETH Zurich).
Project background
This position is part of the SNSF Ambizione project MiNI – Multimodal Neuromuscular Interface , in close collaboration
with groups at the Scuola Superiore Sant'Anna in Pisa and Imperial College London.
Application context
MiNI explores the convergence of ML, embedded intelligence, and human sensing to develop a platform for real-time
neuromuscular signal interpretation. Current AR/XR systems depend mainly on camera-based tracking, accelerometers or
sEMG and are constrained by power consumption and limited robustness. MiNI introduces a multimodal approach that
integrates electrical (sEMG), mechanical (ultrasound), and complementary sensor data for high-fidelity movement
estimation, enabling natural, efficient human–machine interaction.
Research focus and methodology
The PhD research will establish the algorithmic and system-level foundations for intelligent, energy-aware sensing
nodes capable of learning and adaptation at the edge. Core directions include:
- Model foundations: Pretraining strategies, foundation models and domain adaptation for ultrasound and complementary
modalities.
- Sensor fusion: Multimodal frameworks that integrate heterogeneous data streams for robust, efficient inference.
- Efficiency and adaptation: Lightweight, real-time models using quantization, compression, and adaptive execution.
- Hardware–algorithm co-design: Cross-layer optimization aligning ML algorithms with embedded hardware.
- Distributed and agentic embedded AI: Networks of autonomous micro-agents for cooperative sensing and learning.
Job description
- Conduct experimental and theoretical research along the project’s core directions.
- Implement and validate developed methods on embedded platforms and sensor prototypes.
- Collaborate with experts in embedded systems, digital IC design, and applications to realize end-to-end
human–computer interaction demonstrators.
- Publish findings in leading high impact journals (eg. IEEE, ACM, Nature) and conferences (e.g., NeurIPS, SenSys).
- Supervise Bachelor’s and Master’s students on related topics.
Profile
- Master’s degree in computer science, electrical engineering, or related discipline.
- Strong background in machine learning, deep learning, and optimization.
- Interest in cross-domain research linking ML, systems, and hardware design.
- Experience in one or more of: LLMs, AI agents, embedded ML, physical modelling and simulation
- Strong programming skills in Python and C/C++, familiarity with ML deployment frameworks (e.g., ONNX Runtime,
TFLite) is advantageous.
- Independent, creative, and analytical mindset with excellent communication skills in English.
Workplace
Workplace
We offer
The Digital Circuits and Systems Group at ETH Zurich focuses on advancing energy-efficient computing across the full
performance spectrum, from ultra-low-power IoT nodes to high-performance systems. Its research targets an
order-of-magnitude improvement in efficiency through parallelization, near-sensor processing, and heterogeneous
architectures with specialized accelerators.
chevron_right Working, teaching and research at ETH Zurich
We value diversity and sustainability
In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value
diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students
are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open
environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently
working towards a climate-neutral future.
Curious? So are we.
We look forward to receiving your online application with the following documents:
- Motivation Letter – A letter that clearly demonstrates your motivation and suitability for the position and descibes
one scientific achievement to date (max. 2 pages)
- Curriculum Vitae (CV) – Concise and technically detailed CV, emphasizing relevant experience, skills, and
achievements. (max. 2 pages)
- References – Names and contact emails of two referees who can provide professional references (We will contact them
as we review the applications).
- Portfolio – Overview with project summaries and links to code repositories, datasets, and other supporting materials.
- (Optional) Publications – brief overview of your published works, preprints, or conference contributions,
highlighting those most relevant to the project's research direction.
- Transcripts and Degree Certificates – Official records of all completed degrees.
File Naming and Upload Instructions
- Submit each document as a separate PDF file. Use PDF format only (no Word, ZIP, or image files).
- Name all files strictly according to the format below:
- [Firstname]_[Lastname]_[DocumentType].pdf
- Ensure filenames contain no spaces or special characters other than underscores.
Examples:
- Bianca_TheBear_MotivationLetter.pdf
- Bianca_TheBear_CV.pdf
- Bianca_TheBear_References.pdf
- Bianca_TheBear_Portfolio.pdf
- Bianca_TheBear_Publications.pdf
- Bianca_TheBear_Transcripts.pdf
Please note that we exclusively accept applications submitted through our online application portal. Applications via
email or postal services will not be considered.
Further information about Integrated Systems Laboratory can be found on our website.
We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial
intelligence.
About ETH Zürich
ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our
excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000
people from more than 120 countries find our university to be a place that promotes independent thinking and an
environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we
work together to develop solutions for the global challenges of today and tomorrow.
About ETH Zürich
ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our
excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000
people from more than 120 countries find our university to be a place that promotes independent thinking and an
environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we
work together to develop solutions for the global challenges of today and tomorrow.
100%, Zurich, fixed-term
print Drucken
We have an open PhD position at the intersection of machine learning, embedded intelligence and human–computer
interaction. The project will explore how learning systems can become more adaptive, efficient and context-aware,
creating the foundations for the next generation of wearable and augmented reality platforms.
The research focuses on developing novel ML methods that learn from limited resources, adapt locally to users and
environments, and remain computationally efficient. These models will be explored and benchmarked in software, with
selected approaches translated into embedded prototypes to demonstrate real-world feasibility. The overarching goal is
to bridge high-level algorithmic innovation with energy-aware hardware deployment, enabling intelligent sensor systems
that act as autonomous micro-agents for perception and communication.
Candidates ideally have a background in computer science , electrical engineering, or related fields, and a strong
interest in machine learning, optimization, or intelligent systems. Prior experience with embedded platforms, signal
processing, or model compression is a plus. We value creativity, curiosity, and the ability to connect ideas across
disciplines.
The PhD will be co-supervised by Dr Christoph Leitner and Prof Luca Benini in the Digital Circuits and Systems Group
at the Integrated Systems Laboratory (D-ITET, ETH Zurich).
Project background
This position is part of the SNSF Ambizione project MiNI – Multimodal Neuromuscular Interface , in close collaboration
with groups at the Scuola Superiore Sant'Anna in Pisa and Imperial College London.
Application context
MiNI explores the convergence of ML, embedded intelligence, and human sensing to develop a platform for real-time
neuromuscular signal interpretation. Current AR/XR systems depend mainly on camera-based tracking, accelerometers or
sEMG and are constrained by power consumption and limited robustness. MiNI introduces a multimodal approach that
integrates electrical (sEMG), mechanical (ultrasound), and complementary sensor data for high-fidelity movement
estimation, enabling natural, efficient human–machine interaction.
Research focus and methodology
The PhD research will establish the algorithmic and system-level foundations for intelligent, energy-aware sensing
nodes capable of learning and adaptation at the edge. Core directions include:
- Model foundations: Pretraining strategies, foundation models and domain adaptation for ultrasound and complementary
modalities.
- Sensor fusion: Multimodal frameworks that integrate heterogeneous data streams for robust, efficient inference.
- Efficiency and adaptation: Lightweight, real-time models using quantization, compression, and adaptive execution.
- Hardware–algorithm co-design: Cross-layer optimization aligning ML algorithms with embedded hardware.
- Distributed and agentic embedded AI: Networks of autonomous micro-agents for cooperative sensing and learning.
Job description
- Conduct experimental and theoretical research along the project’s core directions.
- Implement and validate developed methods on embedded platforms and sensor prototypes.
- Collaborate with experts in embedded systems, digital IC design, and applications to realize end-to-end
human–computer interaction demonstrators.
- Publish findings in leading high impact journals (eg. IEEE, ACM, Nature) and conferences (e.g., NeurIPS, SenSys).
- Supervise Bachelor’s and Master’s students on related topics.
Profile
- Master’s degree in computer science, electrical engineering, or related discipline.
- Strong background in machine learning, deep learning, and optimization.
- Interest in cross-domain research linking ML, systems, and hardware design.
- Experience in one or more of: LLMs, AI agents, embedded ML, physical modelling and simulation
- Strong programming skills in Python and C/C++, familiarity with ML deployment frameworks (e.g., ONNX Runtime,
TFLite) is advantageous.
- Independent, creative, and analytical mindset with excellent communication skills in English.
Workplace
Workplace
We offer
The Digital Circuits and Systems Group at ETH Zurich focuses on advancing energy-efficient computing across the full
performance spectrum, from ultra-low-power IoT nodes to high-performance systems. Its research targets an
order-of-magnitude improvement in efficiency through parallelization, near-sensor processing, and heterogeneous
architectures with specialized accelerators.
chevron_right Working, teaching and research at ETH Zurich
We value diversity and sustainability
In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value
diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students
are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open
environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently
working towards a climate-neutral future.
Curious? So are we.
We look forward to receiving your online application with the following documents:
- Motivation Letter – A letter that clearly demonstrates your motivation and suitability for the position and descibes
one scientific achievement to date (max. 2 pages)
- Curriculum Vitae (CV) – Concise and technically detailed CV, emphasizing relevant experience, skills, and
achievements. (max. 2 pages)
- References – Names and contact emails of two referees who can provide professional references (We will contact them
as we review the applications).
- Portfolio – Overview with project summaries and links to code repositories, datasets, and other supporting materials.
- (Optional) Publications – brief overview of your published works, preprints, or conference contributions,
highlighting those most relevant to the project's research direction.
- Transcripts and Degree Certificates – Official records of all completed degrees.
File Naming and Upload Instructions
- Submit each document as a separate PDF file. Use PDF format only (no Word, ZIP, or image files).
- Name all files strictly according to the format below:
- [Firstname]_[Lastname]_[DocumentType].pdf
- Ensure filenames contain no spaces or special characters other than underscores.
Examples:
- Bianca_TheBear_MotivationLetter.pdf
- Bianca_TheBear_CV.pdf
- Bianca_TheBear_References.pdf
- Bianca_TheBear_Portfolio.pdf
- Bianca_TheBear_Publications.pdf
- Bianca_TheBear_Transcripts.pdf
Please note that we exclusively accept applications submitted through our online application portal. Applications via
email or postal services will not be considered.
Further information about Integrated Systems Laboratory can be found on our website.
We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial
intelligence.
About ETH Zürich
ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our
excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000
people from more than 120 countries find our university to be a place that promotes independent thinking and an
environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we
work together to develop solutions for the global challenges of today and tomorrow.
About ETH Zürich
ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our
excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000
people from more than 120 countries find our university to be a place that promotes independent thinking and an
environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we
work together to develop solutions for the global challenges of today and tomorrow.