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Simulation Phd Jobs in New York (NOW HIRING)

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Simulation Phd information

What is a Simulation PhD?

A Simulation PhD is a doctoral degree focused on the development, application, and analysis of simulation techniques across various fields such as engineering, physics, computer science, or healthcare. Students in this program conduct advanced research to create or improve models that simulate real-world processes or systems. The degree typically involves coursework, comprehensive exams, and an original dissertation contributing new knowledge to the field of simulation. Graduates often pursue careers in academia, research, industry, or government, where they apply simulation to solve complex problems.

What are the key skills and qualifications needed to thrive as a Simulation PhD?

To thrive as a Simulation PhD, you need advanced expertise in simulation modeling, mathematics, and data analysis, usually supported by a doctoral degree in a relevant field such as engineering or computer science. Proficiency with technical tools like MATLAB, Simulink, Python, or specialized simulation software, along with experience in programming and statistical analysis, is essential. Strong problem-solving skills, critical thinking, and effective communication are crucial soft skills for collaborating with interdisciplinary teams and presenting complex results. These abilities are important to drive innovation, ensure accurate research outcomes, and translate simulation results into practical solutions.

What are the typical research collaboration opportunities available to a Simulation PhD within academia or industry?

Simulation PhD holders often work closely with multidisciplinary teams, including engineers, computer scientists, and domain experts, to develop and validate complex models. In academia, collaboration may involve joint research projects, co-authored publications, or participation in research consortia. In industry, Simulation PhDs might partner with product development teams, data analysts, or IT professionals to solve real-world problems and optimize processes. These collaborations not only enhance research impact but also provide valuable networking and career advancement opportunities.

What job categories do people searching Simulation Phd jobs in New York look for?

The top searched job categories for Simulation Phd jobs in New York are:

What cities in New York are hiring for Simulation Phd jobs?

Cities in New York with the most Simulation Phd job openings:

Infographic showing various Simulation Phd job openings in New York as of June 2026, with employment types broken down into 90% Full Time, 9% Part Time, and 1% Contract. Highlights an 92% Physical, 3% Hybrid, and 5% Remote job distribution.

Research Scientist, Artificial Intelligence (PhD)

Synaptrix Labs

New York, NY

$85K - $150K/yr

Full-time

Re-posted 17 days ago


Job description

About Synaptrix Labs Inc.

Synaptrix is on a mission to revolutionize brain-computer interfaces through non-invasive approaches. We believe that the power to diagnose and treat neurological conditions safely, and to expand human potential, will become a reality with the right fusion of deep learning, signal processing, and computational neuroscience.

We're seeking a full time Research Scientist, Artificial Intelligence (PhD) to join our growing team of researchers & engineers. If you're passionate about shaping the future of brain-computer interfaces and excited by the potential of deep learning in neurotechnology, we want to hear from you!

Responsibilities:

  • Design, prototype, and optimize state-of-the-art AI systems for neural decoding, including diffusion models, graph neural networks, contrastive/self-supervised frameworks, and transformer-based sequence models.
  • Conduct foundational research on neural time-series representation learning: build architectures that extract latent dynamics from EEG, EMG, or related biosignals.
  • Develop high-fidelity simulation environments for testing decoding algorithms, incorporating stochastic signal noise and realistic biophysical constraints.
  • Scale model training across multi-GPU and multi-node clusters using PyTorch Distributed, DeepSpeed, or JAX/Flax; profile and tune system performance for sub-10 ms inference latency.
  • Build and maintain end-to-end research pipelines for large-scale signal datasets, including preprocessing, artifact rejection, and multimodal fusion with video, audio, and IMU data.
  • Collaborate with neuroscientists and hardware engineers to integrate learned models into real-time BCI control loops and embedded systems.
  • Contribute to core ML infrastructure: experiment tracking, model versioning, dataset lineage, and reproducibility standards.
  • Publish at top-tier ML or neurotech venues (NeurIPS, ICLR, Nature Neuro, EMBC) and present findings to the research community.

Minimum Qualifications:

  • PhD or equivalent deep technical expertise in Machine Learning, Artificial Intelligence, Computer Science, Computational Neuroscience, or related fields.
  • Strong command of PyTorch or JAX, with experience implementing custom training loops, loss functions, and model architectures.
  • Proven ability to conduct end-to-end research, from conceptual design to reproducible experiments and evaluation.
  • Strong mathematical foundations in linear algebra, probability, optimization, and information theory.
  • Experience working with high-dimensional time-series or sensory data (EEG, speech, video, motion capture, etc.).
  • Skilled in Python, NumPy, Pandas, and scientific computing workflows; experience with CUDA or low-level GPU debugging is highly valued.
  • Demonstrated ability to operate independently on open-ended problems and drive original research with limited supervision.

Preferred Qualifications:

  • Deep familiarity with neural signal modeling, neural decoding, or biosignal preprocessing (EEG/MEG/ECoG/EMG).
  • Experience designing self-supervised or generative models (diffusion, VAEs, contrastive, masked modeling) for noisy, non-stationary data.
  • Background in reinforcement learning, optimal control, or human-in-the-loop systems, especially in continuous domains.
  • Publications or preprints in top venues (NeurIPS, ICML, ICLR, CVPR, EMBC, Nature Neuro).
  • Familiarity with distributed training, mixed-precision, multi-GPU orchestration, and cloud ML infrastructure (AWS/GCP/Azure).
  • Contributions to open-source ML frameworks or custom CUDA kernels.
  • Understanding of neural signal acquisition hardware, embedded inference, or edge ML deployment.
  • Track record of curiosity-driven, independent research resulting in practical systems or open-source codebases.

About our Culture:

At Synaptrix Labs, we celebrate curiosity, open collaboration, and scientific rigor. Our interdisciplinary team spans neuroscience, AI, and clinical research, and we are united by the belief that non-invasive BCI is the key to unlocking a new era in healthcare, accessibility, and human augmentation.

Expected Compensation:

The base salary for this role is anticipated to fall within the following range. Actual compensation will depend on your experience, technical expertise, and relevant education or training. In addition to base pay, Synaptrix offers equity to all full-time employees, reflecting our commitment to shared success and long-term company growth.

Base Salary Range:

$85,000 - $150,000 USD

What We Offer:

  • An opportunity to change the world and work with some of the smartest and most talented experts from different fields
  • Growth potential; we rapidly advance team members who have an outsized impact
  • Paid holidays, unlimited PTO