PhD Position: Predicting the Energy Consumption of AI Algorithms

NOLAI; Nijmegen; On-site; EUR 34,138 - 43,311; Full time

Benefits offered by this job

8% holiday allowance

8.3% end-of-year bonus

Additional leave days

Job summary

A technology research institute in Nijmegen is offering a PhD position focused on predicting the energy consumption of AI algorithms. Applicants should have a Master's degree in Computing Science and an interest in sustainable AI practices. The role involves developing methodologies to assess the environmental impact during the design phase of AI systems, working closely with a multidisciplinary team. The position offers a starting salary of €3,059 which increases to €3,881 in the fourth year, alongside attractive benefits like additional leave days.

Qualifications

• Hold a Master's degree in a relevant field.
• Knowledge of security and privacy is preferred.
• Desire to work in an applied research setting.

Responsibilities

• Develop methods for predicting energy consumption of AI systems.
• Assess environmental impact during the design phase of AI systems.
• Collaborate with a team on AI prototypes and supporting infrastructure.

Skills

Knowledge of security and privacy

Eagerness to work in a multidisciplinary setting

Education

Master's degree in Computing Science or Information Science

Job description

PhD Position: Predicting the Energy Consumption of AI Algorithms

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PhD Position: Predicting the Energy Consumption of AI Algorithms

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Are you eager to make AI more sustainable? As a PhD Candidate, you will develop innovative methods for predicting and reducing the energy consumption of large-scale AI systems during their design phase. Your work will help shape environmentally responsible AI technologies for the future!

The goal of your PhD project is to develop methods to steer developments of large AI systems in such a way that they are environmentally sustainable. To this end, different designs of AI systems should be assessed during the design phase. Data flow diagrams used at NOLAI capture the processing, storage and transmission of data: key elements in assessing the environmental impact of IT systems. You can extend these data flow diagrams with the expected energy consumption so different design variants can be considered by the team working on these systems.

You will perform measurements of AI algorithms to fill in the unknowns uncovered in such a data flow diagram. The energy scalability of the core algorithms of a new nationwide AI system can be predicted using generated data sets of different sizes and measuring the environmental impact. This impact can be measured and calculated by our Software Energy Lab, which has multiple test machines with GPUs and AI accelerators.

The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will be used to calculate the theoretical maximum energy efficiency of an AI algorithm for a specific architecture/accelerator and will help you optimise the energy consumption of AI algorithms. To make experimentation with implementations of algorithms easier, you will leverage our existing approach to generate code from a single source code for multiple architectures and accelerators, called SaC.

You will join a team consisting of scientific staff and PhD candidates from different disciplines and will collaborate with a small team of software developers working on AI prototypes and the required supportive infrastructure.

Are you eager to make AI more sustainable? As a PhD Candidate, you will develop innovative methods for predicting and reducing the energy consumption of large-scale AI systems during their design phase. Your work will help shape environmentally responsible AI technologies for the future!

The goal of your PhD project is to develop methods to steer developments of large AI systems in such a way that they are environmentally sustainable. To this end, different designs of AI systems should be assessed during the design phase. Data flow diagrams used at NOLAI capture the processing, storage and transmission of data: key elements in assessing the environmental impact of IT systems. You can extend these data flow diagrams with the expected energy consumption so different design variants can be considered by the team working on these systems.

You will perform measurements of AI algorithms to fill in the unknowns uncovered in such a data flow diagram. The energy scalability of the core algorithms of a new nationwide AI system can be predicted using generated data sets of different sizes and measuring the environmental impact. This impact can be measured and calculated by our Software Energy Lab, which has multiple test machines with GPUs and AI accelerators.

The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will be used to calculate the theoretical maximum energy efficiency of an AI algorithm for a specific architecture/accelerator and will help you optimise the energy consumption of AI algorithms. To make experimentation with implementations of algorithms easier, you will leverage our existing approach to generate code from a single source code for multiple architectures and accelerators, called SaC.

You will join a team consisting of scientific staff and PhD candidates from different disciplines and will collaborate with a small team of software developers working on AI prototypes and the required supportive infrastructure.

Does this sound like you?

• You hold a Master's degree in Computing Science or Information Science (or a closely related degree) and have knowledge of and an interest in security and privacy.
• You are eager to work in a multidisciplinary setting with AI experts, social scientists, schools and commercial partners.
• You are interested in research in an applied setting, making sure the contributions are practical and sustainable.

What we offer you

• We will give you a temporary employment contract (1.0 FTE) of 1.5 years, after which your performance will be evaluated. If the evaluation is positive, your contract will be extended by 2.5 years (4-year contract).
• You will receive a starting salary of €3,059 gross per month based on a 38-hour working week, which will increase to €3,881 in the fourth year (salary scale P).
• You will receive an 8% holiday allowance and an 8,3% end-of-year bonus.
• You will receive extra days off. With full-time employment, you can choose between30 or 41 daysof annual leave instead of the statutory 20.

Seniority level

• Seniority level

  Internship

Employment type

• Employment type

  Full-time

Job function

• Job function

  Research, Analyst, and Information Technology

• Industries

  Primary and Secondary Education

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