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research position: AI Research Scientist - Cognizant AI Labs
Where: San Francisco, California, United States,
United States
What: Cognizant AI Labs:
Cognizant works with an incredible diversity of organizations across the globe, using AI to improve decision-making, robustness, forecasting, and growth at every level of operation.
Within Cognizant, Cognizant AI Labs serves as the center of excellence for pioneering AI research. The team works to develop novel approaches to solve both fundamental scientific problems and challenges from real-world applications.
The work done by Cognizant AI Labs serves to inspire and catalyze real-world applications implemented by Cognizant, and reciprocally, real-world challenges encountered in Cognizant’s diverse ecosystem of applications serve to inspire foundational research at Cognizant AI Labs.
Your role:
As a research scientist, you will work with the Cognizant AI Labs research team to develop novel approaches to solving fundamental scientific problems and challenges from real-world applications, using core technologies such as LLMs, evolutionary algorithms, and other machine learning and AI techniques. With an explicit focus on AI for Good applications alongside basic research, the team envisions a world where AI systems are safe, robust, sustainable, long-lived, and inspiring. The team's current research is focused around areas including, but not limited to:
Key Responsibilities:
Qualifications:
Important Note: The application URL may occasionally expire or show "position filled". No worries! We are still hiring! If you are interested in this position, or have any questions about this job posting, feel free to contact the hiring manager Xin Qiu (xin.qiu@cognizant.com) or Elliot Meyerson (elliot.meyerson@cognizant.com).
Who: https://careers.cognizant.com/global-en/jobs/00058670981/ai-research-scientist/
When: Until 2024-12-30 11:00
Presented at next GECCO?: yes
post-doc: Post-Doc Position - Plasticity of an insect brain: a neuro-evolutionary approach
Where: Toulouse, France,
France
What: Duration: 2 years + 2 renewable years
Starting date: Jan-Jun 2025
Summary: The visual navigation behaviour of insects such as ants and bees is one of the most remarkable example of the ability of mini-brains to produce sophisticated and robust behaviours in complex environments. We have excellent recordings of the trajectory effected by these insects as they venture out of their nest to forage for food. Also, thanks to the development of the neurobiological tools in insects, we have an increasingly detailed description of the circuitry of these mini-brains. These networks, made up of a large number of interacting units (the neurons), are the site of a highly dynamic internal activity that enable an efficient coupling of the organism with its environment. Current insect neural models explain well the ability of the ants to navigate at a t-time, but do not explain the ability of insects to self-develop their navigational skills and compensate to impairments, that is, their plasticity and resilience. This project aims at developing plastic neural models that capture the insect navigational plasticity and resilience.
The neuro-evolutionary approach: simulation of navigating ant-agent models equipped with evolving, plastic neural networks models, which process visual information and drive motor action. These virtual agents will navigate in virtual reconstructions of ants’ natural environment, so as to generate feedbacks from motor action to perception. The evolved agents’ behaviours will be compared with the observed behaviours of real ants. The evolved neural architecture will be tuned towards and compared with real insect neural circuits.
Environment: The position is funded by the ERC-consolidator-grant project RESILI-ANT, led by Dr. Antoine Wystrach, expert in ant navigation neural models. The participant will be hosted within the project team in the University of Toulouse Paul Sabatier, and work closely with the PI Antoine Wystrach, another post-doc and two PhD students performing behavioural studies in the field and in virtual reality, and a research engineer.
Applicant profile
- Strong background in neuroevolutionary algorithms.
- Suggested specifics skills: NEAT, NAS, Quality Diversity, Recurrent Networks, Plasticity rules.
- Ability to develop custom neuroevolutionary approaches
- Use of computing clusters.
- General skills in Python.
- Skills in 3D image rendering (optional)
Who: Antoine Wystrach <antoine.wystrach@univ-tlse3.fr>, Dennis Wilson <dennis.wilson@isae.fr>
When: Until 2024-11-30 10:00
Presented at next GECCO?: yes
PhD: Theory or application of evolutionary computation for multimodal or multiobjective optimization
Where: University of New South Wales, Canberra,
Australia
What: PhD positions are available for candidates with a research background and interest in theory or applications of evolutionary computation for multimodal or multiobjective optimization. Scholarships are also available for applicants with strong educational/research backgrounds and interests.
Who: Ali Ahrari
When: Until 2024-12-30 08:00
Presented at next GECCO?: yes
PhD: Theory of Evolutionary Algorithms with Applications to Game Theory and AI
Where: Birmingham,
United Kingdom
What: Applications are invited for fully-funded PhD studentships in the
School of Computer Science, University of Birmingham within the theory
of (co)-evolutionary algorithms.
About the Project
Co-evolutionary algorithms and other randomised search heuristics have
been successfully applied to both classical optimisation as well as
game-theoretic and adversarial optimisation scenarios. However, the
theoretical understanding of these methods has been limited. Recently,
there has been significant progress in analysing — via mathematically
rigorous proofs — the runtime (also called optimisation time) of
these algorithms using techniques from probability theory, randomised
algorithms, and computational complexity. Results about the runtime
give insights into how the behaviour of these algorithms depends on
their parameter settings and the characteristics of the underlying
optimisation problem or game.
The successful candidate will contribute, together with other members
of our research group and international academic and industrial
partners, towards developing the theoretical foundation necessary for
the design of efficient and reliable (co)-evolutionary algorithms.
The PhD studentships will be associated with the UKRI-funded Turing AI
Acceleration Fellowship project "Rigorous Time-Complexity Analysis of
co-Evolutionary Algorithms". Our research regularly appears in
conferences within evolutionary computation and general AI, including
GECCO, PPSN, IJCAI, AAAI, and NeurIPS.
About the Candidate
The topic is mathematically challenging and requires an excellent
degree in computer science, mathematics or related discipline. In
particular, a strong background in probability theory, discrete
mathematics, game theory, and/or theoretical computer science is
desirable.
About the School of Computer Science, University of Birmingham
Our research is ranked 3rd across all UK universities according to the
latest UK-wide Research Excellence Framework. Education is ranked 7th
in the UK for computer science, according to 2023 tables. While
pursuing excellence in research and education, we also aim to optimise
our positive impact on society--examples include collaborations with
industry partners and charities, commercialisation activities and an
extensive wider participation programme. To support its aims, the
School offers a vibrant, open and intellectually stimulating research
environment.
Application deadline
December 5th, 2024
Further Information
https://www.birmingham.ac.uk/schools/computer-science/postgraduate-research
Prior to submitting their formal applications, candidates are
encouraged to send an email to Per Kristian Lehre with the subject
line "PhD Position" with their CV, transcript, and a brief explanation
of their research interests.
Who: Per Kristian Lehre, p.k.lehre@bham.ac.uk
When: Until 2024-12-04 18:00
Presented at next GECCO?: yes
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research position: AI Research Scientist - Cognizant AI Labs
Where: San Francisco, California, United States,
United StatesWhat: Cognizant AI Labs:
Cognizant works with an incredible diversity of organizations across the globe, using AI to improve decision-making, robustness, forecasting, and growth at every level of operation.
Within Cognizant, Cognizant AI Labs serves as the center of excellence for pioneering AI research. The team works to develop novel approaches to solve both fundamental scientific problems and challenges from real-world applications.
The work done by Cognizant AI Labs serves to inspire and catalyze real-world applications implemented by Cognizant, and reciprocally, real-world challenges encountered in Cognizant’s diverse ecosystem of applications serve to inspire foundational research at Cognizant AI Labs.
Your role:
As a research scientist, you will work with the Cognizant AI Labs research team to develop novel approaches to solving fundamental scientific problems and challenges from real-world applications, using core technologies such as LLMs, evolutionary algorithms, and other machine learning and AI techniques. With an explicit focus on AI for Good applications alongside basic research, the team envisions a world where AI systems are safe, robust, sustainable, long-lived, and inspiring. The team's current research is focused around areas including, but not limited to:
- AI for decision-making
- AI orchestration
- Trustworthy AI
- Open-ended AI
- Sequential/time-series domains
- Multi-agent systems
- ALife
- LLMs
- Evolutionary computation
Key Responsibilities:
- Work with the Advanced AI Labs research team to develop original ideas that can contribute to the AI community
- Design experiments and evaluation methodologies for testing these ideas
- Implement novel algorithms and evaluation frameworks
- Manage experiments, analyze results, and iterate rapidly
- Communicate ideas and results to a larger audience
- Publish papers based on this work
- Advise AI engineers on the development of practical applications
Qualifications:
- Should have a PhD in Computer Science or another technical field.
- Passion for AI research and AI for Good.
- 3+ years of experience in AI or ML research.
- Publications at venues such as ICLR, NeurIPS, GECCO, ALife, AAAI, IJCAI, ICML, etc.
- Strong implementation skills.
- Experience with LLMs.
- Strong problem-solving and analytic skills.
- Strong attention to detail and ability to work independently.
- Excellent verbal and written communication skills.
Important Note: The application URL may occasionally expire or show "position filled". No worries! We are still hiring! If you are interested in this position, or have any questions about this job posting, feel free to contact the hiring manager Xin Qiu (xin.qiu@cognizant.com) or Elliot Meyerson (elliot.meyerson@cognizant.com).
Who: https://careers.cognizant.com/global-en/jobs/00058670981/ai-research-scientist/
When: Until 2024-12-30 11:00
Presented at next GECCO?: yes
post-doc: Post-Doc Position - Plasticity of an insect brain: a neuro-evolutionary approach
Where: Toulouse, France,
FranceWhat: Duration: 2 years + 2 renewable years
Starting date: Jan-Jun 2025
Summary: The visual navigation behaviour of insects such as ants and bees is one of the most remarkable example of the ability of mini-brains to produce sophisticated and robust behaviours in complex environments. We have excellent recordings of the trajectory effected by these insects as they venture out of their nest to forage for food. Also, thanks to the development of the neurobiological tools in insects, we have an increasingly detailed description of the circuitry of these mini-brains. These networks, made up of a large number of interacting units (the neurons), are the site of a highly dynamic internal activity that enable an efficient coupling of the organism with its environment. Current insect neural models explain well the ability of the ants to navigate at a t-time, but do not explain the ability of insects to self-develop their navigational skills and compensate to impairments, that is, their plasticity and resilience. This project aims at developing plastic neural models that capture the insect navigational plasticity and resilience.
The neuro-evolutionary approach: simulation of navigating ant-agent models equipped with evolving, plastic neural networks models, which process visual information and drive motor action. These virtual agents will navigate in virtual reconstructions of ants’ natural environment, so as to generate feedbacks from motor action to perception. The evolved agents’ behaviours will be compared with the observed behaviours of real ants. The evolved neural architecture will be tuned towards and compared with real insect neural circuits.
Environment: The position is funded by the ERC-consolidator-grant project RESILI-ANT, led by Dr. Antoine Wystrach, expert in ant navigation neural models. The participant will be hosted within the project team in the University of Toulouse Paul Sabatier, and work closely with the PI Antoine Wystrach, another post-doc and two PhD students performing behavioural studies in the field and in virtual reality, and a research engineer.
Applicant profile
- Strong background in neuroevolutionary algorithms.
- Suggested specifics skills: NEAT, NAS, Quality Diversity, Recurrent Networks, Plasticity rules.
- Ability to develop custom neuroevolutionary approaches
- Use of computing clusters.
- General skills in Python.
- Skills in 3D image rendering (optional)
Who: Antoine Wystrach <antoine.wystrach@univ-tlse3.fr>, Dennis Wilson <dennis.wilson@isae.fr>
When: Until 2024-11-30 10:00
Presented at next GECCO?: yes
PhD: Theory or application of evolutionary computation for multimodal or multiobjective optimization
Where: University of New South Wales, Canberra,
AustraliaWhat: PhD positions are available for candidates with a research background and interest in theory or applications of evolutionary computation for multimodal or multiobjective optimization. Scholarships are also available for applicants with strong educational/research backgrounds and interests.
Who: Ali Ahrari
When: Until 2024-12-30 08:00
Presented at next GECCO?: yes
PhD: Theory of Evolutionary Algorithms with Applications to Game Theory and AI
Where: Birmingham,
United KingdomWhat: Applications are invited for fully-funded PhD studentships in the
School of Computer Science, University of Birmingham within the theory
of (co)-evolutionary algorithms.
About the Project
Co-evolutionary algorithms and other randomised search heuristics have
been successfully applied to both classical optimisation as well as
game-theoretic and adversarial optimisation scenarios. However, the
theoretical understanding of these methods has been limited. Recently,
there has been significant progress in analysing — via mathematically
rigorous proofs — the runtime (also called optimisation time) of
these algorithms using techniques from probability theory, randomised
algorithms, and computational complexity. Results about the runtime
give insights into how the behaviour of these algorithms depends on
their parameter settings and the characteristics of the underlying
optimisation problem or game.
The successful candidate will contribute, together with other members
of our research group and international academic and industrial
partners, towards developing the theoretical foundation necessary for
the design of efficient and reliable (co)-evolutionary algorithms.
The PhD studentships will be associated with the UKRI-funded Turing AI
Acceleration Fellowship project "Rigorous Time-Complexity Analysis of
co-Evolutionary Algorithms". Our research regularly appears in
conferences within evolutionary computation and general AI, including
GECCO, PPSN, IJCAI, AAAI, and NeurIPS.
About the Candidate
The topic is mathematically challenging and requires an excellent
degree in computer science, mathematics or related discipline. In
particular, a strong background in probability theory, discrete
mathematics, game theory, and/or theoretical computer science is
desirable.
About the School of Computer Science, University of Birmingham
Our research is ranked 3rd across all UK universities according to the
latest UK-wide Research Excellence Framework. Education is ranked 7th
in the UK for computer science, according to 2023 tables. While
pursuing excellence in research and education, we also aim to optimise
our positive impact on society--examples include collaborations with
industry partners and charities, commercialisation activities and an
extensive wider participation programme. To support its aims, the
School offers a vibrant, open and intellectually stimulating research
environment.
Application deadline
December 5th, 2024
Further Information
https://www.birmingham.ac.uk/schools/computer-science/postgraduate-research
Prior to submitting their formal applications, candidates are
encouraged to send an email to Per Kristian Lehre with the subject
line "PhD Position" with their CV, transcript, and a brief explanation
of their research interests.
Who: Per Kristian Lehre, p.k.lehre@bham.ac.uk
When: Until 2024-12-04 18:00
Presented at next GECCO?: yes
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