PhD Research Intern
DolbySammanfattning
The University of Gothenburg is seeking a researcher for a temporary 9-month position at the Institute of Neuroscience and Physiology, specifically within the Department of Clinical Neuroscience. The role focuses on neurological research related to stroke, involving laboratory work and coordination of stroke research linked to Sahlgrenska University Hospital. This part-time position (20% of full-time) offers an opportunity to contribute to significant research in a collaborative academic settingJobbet i korthet
Anställningstyp
studentjobb
Arbetstid
heltid/deltid
Det här erbjuder vi
Temporary employment for 9 months at 20% of full-time.Opportunity to work in a collaborative research environment.Access to resources and support from a large academic institution.
Ansök senast: Öppet tillsvidare
Publicerad: 2026-10-19
Beskrivning
Join the leader in entertainment innovation and help us design the future. At Dolby, science meets art, and high tech means more than computer code. As a member of the Dolby team, you'll see and hear the results of your work everywhere, from movie theaters to smartphones. We continue to revolutionize how people create, deliver, and enjoy entertainment worldwide. To do that, we need the absolute best talent. We're big enough to give you all the resources you need, and small enough so you can make a real difference and earn recognition for your work. We offer a collegial culture, challenging projects, and excellent compensation and benefits, not to mention a Flex Work approach that is truly flexible to support where, when, and how you do your best work.
Internship: Development and On-Device Deployment of an Autoregressive Model for Real-Time Speech Restoration and Dialog Enhancement
Start: Open
Duration: 3 months (full-time) or 6 months (part-time)
Location: Stockholm
Speech restoration and dialog enhancement are critical components in modern audio experiences, especially in noisy environments or legacy content with degraded quality. Current systems often rely on non-causal, server-side models that require full audio context, limiting their use in real-time or on-device applications. With recent advances in autoregressive language modeling and efficient deep learning deployment, there is growing potential to develop low-latency, high-quality speech enhancement systems that operate in real time and even on consumer devices.
This internship aims to design and implement an autoregressive, large language model (LLM)-based architecture for speech restoration and dialog enhancement. The model will be trained to reconstruct clean speech from degraded inputs by leveraging both acoustic and linguistic context. It will be evaluated against Dolby's current best multimodal system, focusing on perceptual quality, intelligibility, and latency. A key objective is to explore the feasibility of deploying the model on-device through techniques such as model compression, quantization, pruning, and compilation using modern inference frameworks.
Candidate requirements
Dolby Hiring Entity:
Gävlegatan 12A 113 30 Stockholm,
Sweden
Internship: Development and On-Device Deployment of an Autoregressive Model for Real-Time Speech Restoration and Dialog Enhancement
Start: Open
Duration: 3 months (full-time) or 6 months (part-time)
Location: Stockholm
Speech restoration and dialog enhancement are critical components in modern audio experiences, especially in noisy environments or legacy content with degraded quality. Current systems often rely on non-causal, server-side models that require full audio context, limiting their use in real-time or on-device applications. With recent advances in autoregressive language modeling and efficient deep learning deployment, there is growing potential to develop low-latency, high-quality speech enhancement systems that operate in real time and even on consumer devices.
This internship aims to design and implement an autoregressive, large language model (LLM)-based architecture for speech restoration and dialog enhancement. The model will be trained to reconstruct clean speech from degraded inputs by leveraging both acoustic and linguistic context. It will be evaluated against Dolby's current best multimodal system, focusing on perceptual quality, intelligibility, and latency. A key objective is to explore the feasibility of deploying the model on-device through techniques such as model compression, quantization, pruning, and compilation using modern inference frameworks.
Candidate requirements
- Master's or Ph.D. student in Machine Learning, Signal Processing, Computer Science, or a related field.
- Hands-on experience with PyTorch.
- Interest in model optimization for edge deployment using tools such as Triton Inference Server, ONNX, TensorRT, or similar frameworks. Prior experience in this field is a plus.
- Experience fine-tuning LLMs is a plus.
- Knowledge of audio-language modeling is a plus.
Dolby Hiring Entity:
Gävlegatan 12A 113 30 Stockholm,
Sweden
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PhD Research Intern
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Dolby
