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PHD+few years candidates for core research and development of top automotive company originated in USA and having its R&D setup in Bangalore. For its Bangalore lab, we are looking for various openings in software architecture, wireless networks, data mining, operations research, embedded software, cryptography and applied maths.

Please email me at [email protected] with a CC to [email protected]


Tags: AI, applied, architecture, artificial, cryptography, data, embedded, fuzzy, intelligence, logic, More…mathematics, mining, networks, operations, optimization, research, software, wireless

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R&D folks need to learn and use old technologies BEFORE developing new stuff. For example, Calculus-level programming was introduced in 1974. It solves most math problems with easy. Problems are solved in hours not days nor years. Calculus programming will show its users how good or bad their math model may be. Ask R&D engineers or scientists if they have ever heard about Calculus-level programming and your answer will be NO. University professors in 1974 went out of their way to stop students from here about Calculus programming. Why? It has built in numerical methods and thus eliminates need to study numerical methods. These classes should be dropped and get students learn how to test their math models and improve them when using Calculus programming.

Tweaking 1000s of parameters at once is possible with Calculus programming. Math models can be algebraic, ODEs, PDEs, constrained, implicit, nonlinear, nested, etc. It solves them all with built in numerical methods. Leave the work to Calculus-level compilers. Users work on improving their math model and displaying (i.e. plots) their output and that's all. Nice and short.

Management needs to get involved to set company & department goals or objectives. This is called Manage by Objectives. Many engineers & scientists work on projects with no idea what their company, department, nor project objectives are. This hurts ones return-on-invest (ROI). Companies need employees working as teams to improve their ROI. Calculus programming often requires an objective (function) in order to tweak ones parameters and obtain an optimal solution.

Once R&D get the picture of Calculus programming THEN try new stuff. For references, key terms are 'automatic differentiation' and 'operator overloading'. Visit my website for industry example problems.

I hope this helps R&D and others learn what's out their today.



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