**Company: Huawei Research Center Zürich Job Description Summary: Huawei is a technology provider with a global reach, serving over a third of the world’s population. The company’s Zurich Research Center plays a crucial role in driving innovation in ICT solutions. The Neuromorphic Computing Research Lab focuses on developing cutting-edge AI technologies. They are currently seeking a Research Engineer in Neuromorphic and Neuro-inspired Computing Algorithms. The ideal candidate should have a PhD or Master’s degree in a related field, experience in machine learning architectures, language modeling, and programming skills in Python, C/C++, PyTorch, JAX, or TensorFlow. Strong problem-solving and analytical skills, as well as a passion for shaping the future of Neuromorphic Computing and AI, are essential. Fluency in English and the ability to work in a diverse team environment are also required. Expertise in relevant open source projects and a willingness to travel are desired. Job Description: Huawei is a leading global information and communications technology (ICT) solutions provider. Driven by a commitment to sound operations, ongoing innovation, and open collaboration, we have established a competitive ICT portfolio of end-to-end solutions in telecom and enterprise networks, devices, and cloud technology and services. Our ICT solutions, products, and services are used in more than 170 countries and regions, serving over one-third of the world’s population. With 180,000 employees, Huawei is committed to enabling the future information society, and building a Better Connected World. Our Huawei Zurich Research Center is embedded in the European network and plays a pivotal role in driving innovation. The research work of our Labs is carried out not only by Huawei’s internal research staff but also by our academic research partners in universities across Europe. . The Neuromorphic Computing Research Lab is in charge of incubating and developing cutting-edge Computing and… AI technologies. Neuromorphic and Brain-Inspired Computing will play a key role in the next generation AI. More specifically, the Lab conducts research and development on cutting edge Neuro-inspired Computing Algorithms/Materials, including but not limited to brain-mimetic algorithms design, neuromorphic computing elements, spiking neural networks, their training and optimal topology, distributed intelligent agents, knowledge representation and learning algorithms, algorithm optimization and acceleration, multi-modal learning. For our Neuromorphic and Neuro-inspired Computing Research Lab in Zurich, we are looking for a high caliber: Research Engineer in Neuromorphic and Neuro-inspired Computing Algorithms Responsibilities • Research on Neuro-inspired computing and learning frameworks, key technologies and industry best practices • Research on modern recurrent networks (state-space models etc.) as efficient alternatives to self-attention • Research and develop architectures and learning algorithms for next generation AI tools and applications such as language modeling • Keep up to date with the latest research literature, attend conferences, and learn continuously to stay on the forefront of research • Work closely with a multidisciplinary team inside Huawei for integrated solutions; • Develop patents and publish academic articles in top venues (NeurIPS, ICLR, ICML) Requirements • Fresh PhD graduate or Master degree in Neuromorphic Engineering, Computational Neuroscience, Computer science, Mathematics, Machine Learning with practical experience in related topics • Extensive experience and knowledge of modern machine learning architectures (Transformers, recurrent networks, state-space models, etc.) and algorithms • Expertise in language modeling and benchmarks for general sequence models • Expertise in Machine Learning research and associated toolsas demonstrated by an excellent academic track record and publications in top venues (NeurIPS, ICLR, ICML, …), published code bases • Strong skills in some of these programming languages/frameworks: Python, C/C++, PyTorch, JAX, Tensorflow • Able to conduct academic or industry research independently and as reliable team player, quick self-learner • Passion in shaping the future of the Neuromorphic Computing and AI • Enthusiasm in contributing to the future of modern machine learning architectures and algorithms • Excellent problem-solving and analytical skills • Fluent English, excellent communication and presentation skills. • Ability to work in an international, diversified, cross-domain team environment • Willingness and ability to travel are required • Contributions in relevant open source projects If you are enthusiastic about building infrastructure and designing algorithms for future big data and AI, this is the right opportunity pour you
Company:Huawei Research Center Zürich
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Level of experience (years):Senior (5+ years of experience)
“Fine-tuning” means adapting an existing machine learning model for specific tasks or use cases. In this post I’m going to walk you through how you can fine tune a large language model for sentence similarity using some hand annotated test data. This example is in the psychology domain. You need training data consisting of pairs of sentences, and a “ground truth” of how similar you want those sentences to be when you train your custom sentence similarity model.
Hire an NLP developer and untangle the power of natural language in your projects The world is buzzing with the possibilities of natural language processing (NLP). From chatbots that understand your needs to algorithms that analyse mountains of text data, NLP is revolutionising industries across the board. But harnessing this power requires the right expertise. That’s where finding the perfect NLP developer comes in. Post a job in NLP on naturallanguageprocessing.
Natural language processing What is natural language processing? Natural language processing, or NLP, is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. NLP is a branch of AI but is really a mixture of disciplines such as linguistics, computer science, and engineering. There are a number of approaches to NLP, ranging from rule-based modelling of human language to statistical methods. Common uses of NLP include speech recognition systems, the voice assistants available on smartphones, and chatbots.
