Machine Learning Internship Opportunities
First of all, I am going to define what internship is? The internship is the grace period given by any company or business for a limited time period. Internships are usually taken by students and graduates and all others who just want to gain some experience in the practical field. To get an internship one should have an educational background in the relevant field. The individuals can get a permanent job after the internship if the organization sees some potential in the candidates. Now we will come to the term machine learning internship opportunities. The availability of machine learning internship opportunities is very few and getting a machine learning internship opportunities is a very tough job. The rules of machine learning internship vary from country to country.
Machine learning is the research about different computer algorithms that can make computers automate to perform his job. As we know machine learning is the subset of artificial intelligence so this internship is the trending nowadays throughout the world. No one can deny the importance of machine learning or artificial intelligence. The main uses of machine learning are the email filtering and computer vision, in email filtering and computer vision it is infeasible to develop algorithms to perform the required job.
Following are some of the machine learning internship opportunities in different companies:
Machine Learning Internship Opportunities
Baidu Research, headquartered in Silicon Valley, is looking to hire Machine Learning Internship with a background in machine learning or a related application area such as robotics, speech recognition, natural language understanding. Our mission is to advance AI state of the art, realizing a significant impact on hundreds of millions of users. As a research intern at Baidu, you will be uniquely positioned to contribute to this mission and level up your research experience in an industrial environment.
By providing a great learning environment and software/hardware infrastructure we aim to help you and your team make rapid progress on the most challenging AI research problems. We will give you the best possible platform to contribute to this mission through access to bleeding-edge high-performance computing (HPC) research systems, large datasets, and feedback from AI products deployed at scale in Baidu.
Machine Learning Internship Opportunities: Machine Learning-based DPD for Power Amplifier
MERL is looking for a talented intern to work on the next generation Digital-pre-distortion algorithms for power amplifier linearization such as 5G. The development of a DPD system involves aspects of signal processing and statistical algorithm design, RF components and instrumentation, digital hardware and software. It is therefore both a challenging and intellectually rewarding experience. This will involve MATLAB coding, interfacing to test equipment such as power sources, signal generators and analyzers, and construction and calibration of RF component assemblies. The ideal candidate should have knowledge and experience in adaptive signal processing, machine learning, and radio communication. Good practical laboratory skills are needed. RF semiconductor devices and circuit knowledge is a plus. Duration is 3 to 6 months.
Computational Scientist, Machine Learning
The Pfizer Simulation and Modeling Sciences (SMS) group has an opening for a computational methods developer with strong expertise in computational biology, bioinformatics, Artificial Intelligence (AI), and Machine Learning (ML). Working with the biotherapeutics group, the successful candidate will leverage skills in sequence analysis, protein structure modeling, machine learning, and scientific programming to develop AI models and computational tools that enable the design of antibody therapeutics and prediction of biophysical and therapeutic properties. To be successful in this role, the incumbent must be able to effectively collaborate with colleagues with a diverse scientific background, identify problems and opportunities, and combine techniques from computational biology and AI, in particular recent advances in deep learning, to rapidly develop powerful computational solutions.
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