We are currently seeking a hands-on Machine Learning Scientist (Distributed Systems, Tensorflow) for our new research-led startup, focussing on the application of artificial intelligence in the real world; particularly smart city simulations and bots. We're looking for a hardcore Machine Learning Scientist/Engineer who thrives wants to work with the latest technology in multi-agent learning algorithms, Gaussian process and reinforcement learning. As a Machine Learning Scientist/Engineer, you will be a core member of the machine learning team; working closely with the Machine Learning researchers, transforming their algorithmic research into highly innovative products which will be attractive and accessible to the world. Key Skills: Machine Learning Engineer/ML Scientist, Tensorflow, C, C, Java, Python, C#, Distributed Algorithms. Distributed systems, BSc, MSc, MPhil, PhD, Post-Doc, Research, R&D, startup, Multithreading.
Modern machine learning is great for helping scientists sort through huge, unwieldy data sets. But it's less useful for things that require inference or reasoning – both vital to the scientific process. One group of scientists are now trying to fix this problem with a completely new kind of machine learning. This new approach aims to find the underlying algorithmic models that interact and generate data, to help scientists uncover the dynamics of cause and effect. This could aid researchers across a huge range of scientific fields, such as cell biology and genetics, answering the kind of questions that typical machine learning is not designed for.
Deep learning is increasingly dominating technology and has major implications for society. From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology. But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data.
Implementing conventional machine learning approaches to real-world business issues is time consuming, resource-intensive, and hard. It requires specialists from the many areas, including information scientists -- a number of those most sought after professionals at the job market today . Automated machine learning varies which, which makes it simpler to construct and utilize machine learning versions from the actual world by conducting systematic procedures on raw information and picking models that extract the most applicable information from the information -- what's often known as the sign in the sound." Automated machine learning integrates machine learning best practices from top-ranked data scientists to produce information science more accessible across the business. When creating a version with the standard procedure, as you can see from Figure 1, the sole automated task is version coaching .